{"question_id": 1, "text": " I am working on a project where I need to cluster similar images of street art. How can I get the relevant feature vectors from these images for clustering?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate feature vectors for images\\', \\'api_name\\': \\'imagenet_mobilenet_v3_large_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5\\', input_shape=(224, 224, 3), output_shape=(1280,))\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Generate feature vector\\\\nfeature_vector = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'75.3%\\'}, \\'description\\': \\'This API provides a pre-trained image feature vector model, MobileNetV3-Large, from TensorFlow Hub. The model is trained on the ImageNet dataset and can be used to generate feature vectors for images of size 224x224 with 3 channels.\\'}', metadata={})]", "category": "generic"} {"question_id": 2, "text": " We want to develop a simple application where users can take a photo of any object and find the description of it. Help us with creating a model that extracts features from the object's image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_140_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[batch_size, 224, 224, 3]\\', \\'output_shape\\': \\'[batch_size, 1792]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1, activation=\\'sigmoid\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=\\'adam\\', loss=\\'binary_crossentropy\\', metrics=[\\'accuracy\\'])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'80.3%\\'}, \\'description\\': \\'A pre-trained image feature vector model that extracts features from images using MobileNetV2 architecture with a width multiplier of 1.4 and an input size of 224x224 pixels. The model is trained on the ImageNet dataset and achieves 80.3% accuracy.\\'}', metadata={})]", "category": "generic"} {"question_id": 3, "text": " I have a set of food images and I want to create a model that differentiates between different types of cuisine.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 4, "text": " Show me the right API call to generate image feature vectors using MobileNet V2 architecture so that I can perform a classification task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\\\\n\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'This module uses the MobileNet v2 architecture to generate image feature vectors. The model has been pre-trained on the ImageNet dataset and can be used for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 5, "text": " My dog is always getting into things that can be dangerous. Can you help me identify the object my dog is about to eat?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract feature vector from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\\', output_shape=[1280])\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\\', output_shape=[1280]),\\\\n tf.keras.layers.Dense(1)\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\\\nimage = tf.expand_dims(image, 0)\\\\n\\\\nfeature_vector = model.predict(image)\\\\nprint(feature_vector)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to extract a 1280-dimensional feature vector from an image using the MobileNetV2 architecture, which is pre-trained on the ImageNet dataset. The feature vector can be used for various tasks, such as image classification, object detection, and transfer learning.\\'}', metadata={})]", "category": "generic"} {"question_id": 6, "text": " A photo is sent to me, and I would like to know what objects are in the photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 7, "text": " Analyze customer reviews and identify positive and negative sentiments, so please convert the text reviews into vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 8, "text": " Recommend a method to extract image feature vectors with MobileNetV2. \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 9, "text": " Write an example code to use a pre-trained model to convert a sentence \\\"My cat is sitting on the porch\\\" into high-dimensional vectors.\\n###Input: My cat is sitting on the porch\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 10, "text": " Analyze a collection of images of pets to obtain meaningful features and club them into categories based on their appearance and other characteristics.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 11, "text": " We are building a model for car brand recognition. Before training, we need to extract features from car images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 12, "text": " I have a list of sentences and I want to find which ones are most similar. How can I do that efficiently?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Universal Sentence Encoder\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'A list of sentences or phrases\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.80\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 13, "text": " I have a customer service chatbot, and I would like to analyze the similarity between customer queries using NLP.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 14, "text": " I have hundreds of images and I want to group them in categories, but first I need to extract features from those images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 15, "text": " Analyze the given image dataset containing healthy and unhealthy plants by extracting features using pre-trained MobileNetV2 model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 16, "text": " Find out what's in the image taken by a wildlife photographer, so we can determine the main subject of the picture.\\n###Input: An image taken by a wildlife photographer.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 17, "text": " Can you tell me the type of food in this image?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 18, "text": " Identify the semantic theme of the given list of customer support tickets.\\n###Input: ['The payment did not go through.', 'Issues with the payment method.', 'I need help with my payment.', 'Unable to complete payment.', 'Payment error encountered.']\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 19, "text": " I want a system that will read the image of my room and identify all the objects present in the room along with their confidence score.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\", \\'api_arguments\\': \\'image, score_threshold\\', \\'python_environment_requirements\\': \\'Python 3, TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\\\\nresult = detector(image, score_threshold=0.5)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'mAP@0.5IOU = 32.8%\\'}, \\'description\\': \\'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 20, "text": " My company has a database of product descriptions and I want to convert them into vectors for better search functionality.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 21, "text": " I have a project which I need to quickly recognize animals, and I want to use the pre-trained model as a starting point.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 22, "text": " I need an easy way to sort out thousands of travel photos by recognizing landmarks and other objects.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 23, "text": " Develop a mobile application to identify the objects in real-world images and display its category.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 24, "text": " I need to analyze user feedback and compare it to a list of predefined topics.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 25, "text": " We need to recognize the flowers in the pictures from our botanical garden.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image_path\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture, which can recognize 1000 different classes from the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 26, "text": " I want a solid text preprocessing method to make my model more efficient in dealing with different texts.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a text preprocessing model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'model_url\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, World!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load and use the Universal Sentence Encoder model for text preprocessing in TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 27, "text": " A fashion store wants to categorize newly arrived clothes into different classes. Please make sure the right category is assigned to each clothing item.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \\'tf.keras.Sequential([hub.KerasLayer(model_id)])\\', \\'api_arguments\\': [\\'model_id: The identifier of the pre-trained model to load\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/1\\'\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\\\\n\\\\n# Load an image and make a prediction\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\npredictions = model.predict(input_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'A pre-trained image classification model using the Inception V3 architecture, available on TensorFlow Hub. The model can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 28, "text": " To match clothes in my wardrobe, I need to extract features from images and organize them based on similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 29, "text": " We will launch a new product that relies on user reviews, analyze reviews, and generate insights based on their sentiments.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Video classification model\\', \\'api_name\\': \\'Video_classification_model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/Video_classification_model_id/1\\')\", \\'api_arguments\\': [], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'Kinetics-400\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\\'}', metadata={})]", "category": "generic"} {"question_id": 30, "text": " Find me an API that will help me extract features of my images for building a recommendation system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 31, "text": " I want to extract important features from car images of different angles to predict the brand of the car.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 32, "text": " I want to create a phone cover design recommendation system. For that, I need to get some features of cover images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 33, "text": " We are trying to group tweets with similar emotions. How can we represent the text in the tweet so we can quantify the similarity among tweets?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 34, "text": " We are planning a security system for a warehouse, and we need to know the objects in our surveillance feed.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 35, "text": " For arranging my documents, I want to compute the semantic similarity between a set of sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 36, "text": " I would like to group similar user reviews together. To do this, I need to transform the text reviews into high dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained Image Classifier\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"tf.keras.Sequential([hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')])\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'output_shape\\': \\'[1001]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'2.x\\', \\'tensorflow_hub\\': \\'0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', output_shape=[1001])])\\\\nmodel.build([None, 224, 224, 3])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture on TensorFlow Hub. It can be used to classify images into 1000 different categories with high accuracy.\\'}', metadata={})]", "category": "generic"} {"question_id": 37, "text": " I need to find if a word is a positive or negative sentiment. I need to process my text before feeding it to my sentiment analysis model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 38, "text": " I have a collection of photos I took while on vacation, and I want to know what objects are in each photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the ID of the model to be loaded\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Run the detector on an image\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Varies depending on the model version\\'}, \\'description\\': \\'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 39, "text": " Create a list of similar art pictures by comparing the feature vectors of the existing art images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 40, "text": " Discover a person's sentiment after reading their review\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 41, "text": " We have several images of clothing items and want to differentiate them by their features. Generate a model that will create a feature vector for each item.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 42, "text": " Create a mobile app that takes a photo of a plant and identifies its species. To do this, use an image classifier to process the photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Audio processing\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'Load and use an audio embedding model from TensorFlow Hub to process audio data.\\'}', metadata={})]", "category": "generic"} {"question_id": 43, "text": " I want to use an API to find the degree of similarity between sentences. Firstly, I need to obtain their high-dimensional vector representations.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 44, "text": " Let's observe a random street to identify various elements like cars, people and street signs in a photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Semantic segmentation\\', \\'api_name\\': \\'deeplabv3\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3/1\\')\", \\'api_arguments\\': [\\'input_tensor\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nsegmentation = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3/1\\')\\\\ninput_tensor = tf.random.uniform(shape=[1, 1024, 1024, 3], dtype=tf.float32)\\\\noutput = segmentation(input_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'87.5\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art semantic segmentation model that uses atrous convolution and fully connected conditional random fields for dense prediction. It can be used for various image segmentation tasks, such as object segmentation, scene parsing, and person segmentation.\\'}', metadata={})]", "category": "generic"} {"question_id": 45, "text": " Organize my photos taken during the vacation by identifying what the photo is showing.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 46, "text": " Create a document classifier, and identify the genre of a movie poster by classifying the image content.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 47, "text": " Develop a model that will extract feature vectors from images, which we will use later to classify the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\', trainable=False)\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\', trainable=False),\\\\n tf.keras.layers.Dense(1000, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.build([None, 299, 299, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API provides a compact representation of images using the Inception V3 model from TensorFlow Hub. It extracts feature vectors from images, which can be used for various machine learning tasks such as classification and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 48, "text": " I want to build a mobile image recognition app to identify the species of birds in images. Suggest a suitable pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 49, "text": " Explain how to create a image classification model using a pre-trained MobileNetV2 model as a feature extractor.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 50, "text": " There are comments written by users in different languages, and we want to calculate the similarity between pairs of comments.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 51, "text": " Analyze large data sets containing various customer reviews. Transform the text from these reviews into vectors for further analysis like sentiment, similar phrases, and other natural language processing tasks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 52, "text": " I am visiting the Metropolitan Museum of Art and want to quickly identify the category or genre of the artwork by taking a picture of it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'List of sentences\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world\\', \\'I am learning how to use the Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs.\\'}', metadata={})]", "category": "generic"} {"question_id": 53, "text": " Could you provide an embedding for my sentence to be used in semantic similarity analysis?\\n###Input: Someone is learning how to use a Python library.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text for machine learning tasks\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'A list of strings\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'hello world\\', \\'my name is John\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'0.803\\'}, \\'description\\': \\'A text embedding model that converts text into high-dimensional vectors for use in various machine learning tasks. The Universal Sentence Encoder encodes text into 512-dimensional vectors that can be used for tasks such as semantic similarity, classification, or clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 54, "text": " Identify the objects in the image for an image indexing system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nimage = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in an image using the Single Shot MultiBox Detector (SSD) with MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 55, "text": " We want to create an app that can recognize furniture in images. Find an API that can help us.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 56, "text": " Transform a list of product titles into numerical vectors to perform sentiment analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'A pre-trained model for embedding text into high-dimensional vectors that can be used for a wide range of tasks such as sentiment analysis, semantic similarity, and classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 57, "text": " Create a model that can extract features of an image to use in a fashion-based recommendation system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 58, "text": " We are building an app to help people categorize their personal photos into categories. We need to extract features from the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 59, "text": " Design a machine learning model to recognize dog breeds. We need a feature extractor from MobileNet V2 trained on ImageNet dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 60, "text": " I want to find the similarity between user-generated restaurant reviews, so I need to transform these reviews into an appropriate format.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 61, "text": " Compile a report on ongoing political conflicts in the Middle East, using an embedding to identify the key topics.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image pose detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detects human poses in images\\', \\'api_name\\': \\'movenet\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\", \\'api_arguments\\': \\'image_tensor\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\\\\nimage_tensor = ... # Load image tensor\\\\noutputs = model(image_tensor)\\\\nkeypoints = outputs[\\'output_0\\'].numpy()\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'70.4 mAP\\'}, \\'description\\': \\'MoveNet is a family of efficient and accurate pose detection models designed for real-time applications. This model detects 17 key points of a single person in an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 62, "text": " Our school wants to analyse students' Reddit comments to find relevant topics. We need vectors to represent their comments for further processing.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 63, "text": " Recommend me what type of plants are suitable for my garden based on the image input from the garden.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 64, "text": " I am building a chatbot for a cosmetics store and want to understand the semantic similarity between different customer queries.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 65, "text": " From a collection of images, we want to extract a feature vector for each image using MobileNetV2.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 66, "text": " Find and display Cosine Similarity between two different sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 67, "text": " Help me measure the semantic similarity between two sentences, so I can find how closely related they are in meaning.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 68, "text": " Transform the following text into its corresponding high-dimensional vector: \\\"I love exploring new places and trying different cuisines.\\\"\\n###Input: I love exploring new places and trying different cuisines.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input: A tensor of strings, output: A tensor of shape (num_examples, 512)\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I love machine learning.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'SNLI, MultiNLI, SICK\\', \\'accuracy\\': \\'86.3%, 76.6%, 88.0%\\'}, \\'description\\': \\'A pre-trained model for encoding text into a 512-dimensional vector. Useful for various NLP tasks like semantic similarity, sentiment analysis, and text classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 69, "text": " To build an image recognition system, we need a pre-trained model to extract features from images. Provide the API call for this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracts feature vectors from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nfeatures = model.predict(image)\\\\nprint(features)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This API provides a pre-trained Inception V3 model for extracting feature vectors from images. It is built on TensorFlow Hub and requires TensorFlow and TensorFlow Hub packages. The example code demonstrates how to load an image, preprocess it, and extract features using the API.\\'}', metadata={})]", "category": "generic"} {"question_id": 70, "text": " I have a list of sentences that I want to preprocess and make ready to be fed to a machine learning model. Can you do that?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL of the pre-trained model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=(224, 224))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(input_image)\\\\n\\\\n# Decode the predictions\\\\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)\\\\nprint(class_names)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'This API loads a pre-trained image classification model from TensorFlow Hub, allowing users to classify images using the loaded model. The example demonstrates how to load a MobileNetV2 model, preprocess an input image, make predictions, and decode the predictions to obtain class names.\\'}', metadata={})]", "category": "generic"} {"question_id": 71, "text": " We are developing a chatbot, and we need to find the semantically similar sentences from a dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 72, "text": " I am inspecting images of license plates and need to check which ones have plates on them\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 73, "text": " As a programmer, I want to analyze images and determine what objects are in them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 74, "text": " I have a few news articles and I want to see if they're similar. I need a tool to determine the similarity between them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 75, "text": " I need to find similar listings for an Airbnb property, can you find its vector representation for further comparison?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract feature vector from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\\', output_shape=[1280])\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\\', output_shape=[1280]),\\\\n tf.keras.layers.Dense(1)\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\\\nimage = tf.expand_dims(image, 0)\\\\n\\\\nfeature_vector = model.predict(image)\\\\nprint(feature_vector)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to extract a 1280-dimensional feature vector from an image using the MobileNetV2 architecture, which is pre-trained on the ImageNet dataset. The feature vector can be used for various tasks, such as image classification, object detection, and transfer learning.\\'}', metadata={})]", "category": "generic"} {"question_id": 76, "text": " Extract useful information from an image for further processing by minimizing the input.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, useful for extracting features from images. The model is trained on ImageNet dataset and has an accuracy of 71.9%.\\'}', metadata={})]", "category": "generic"} {"question_id": 77, "text": " We need to incorporate an API to differentiate between species of flowers.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 78, "text": " We have customer reviews and need to categorize them. Convert the text into a compatible form for machine learning analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 79, "text": " Can you tell me the breed of my dog if I send you a picture of it? \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 80, "text": " I need to sort my photos into different folders, based on what the main object is in each photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'faster_rcnn_resnet50_v1_640x640\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\\')\\\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image_tensor[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU: 37.1%\\'}, \\'description\\': \\'A pre-trained Faster R-CNN model with ResNet-50 as the backbone for object detection in images. The model is trained on the COCO dataset and can detect objects from 80 different classes.\\'}', metadata={})]", "category": "generic"} {"question_id": 81, "text": " I want to make a recommendation system for similar images, can you recommend an API for image feature extraction?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 82, "text": " Analyze the following list of tweets and create a vector to find if two of them have similar meaning.\\n###Input: [\\\"I love going to the beach\\\", \\\"The beach is my favorite place to visit\\\", \\\"What a good day for ice cream\\\"]\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 83, "text": " I need to analyze numerous article headlines and sort them based on their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nloaded_model = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage_np = np.array(image)\\\\n\\\\ninput_tensor = tf.convert_to_tensor(image_np)\\\\ninput_tensor = input_tensor[tf.newaxis, ...]\\\\n\\\\noutput_dict = loaded_model(input_tensor)\\\\n\\\\nboxes = output_dict[\\'detection_boxes\\'][0].numpy()\\\\nscores = output_dict[\\'detection_scores\\'][0].numpy()\\\\nclasses = output_dict[\\'detection_classes\\'][0].numpy().astype(np.int32)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 84, "text": " We want to know which animal we will find in the picture taken during our vacation.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 85, "text": " Summarize an article by representing its content in a high-dimensional vector format.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the TensorFlow Hub model URL\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Detect objects in the image\\\\nresult = model(image)\\\\n\\\\n# Print detected objects\\\\nfor obj in result[\\'detection_boxes\\']:\\\\n print(obj)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\\'}', metadata={})]", "category": "generic"} {"question_id": 86, "text": " Show me how I can convert this sentence \\\"I enjoy machine learning and natural language processing\\\" into a high-dimensional vector.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 87, "text": " Provide a solution to recognize different types of fruits through image processing.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image_path\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture, which can recognize 1000 different classes from the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 88, "text": " I have recorded some animal sounds and I want to analyze them to identify the animal species.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 89, "text": " Can you provide me with the semantic similarity of two sentences \\\"I love pizza\\\" and \\\"Pizza is my favorite food\\\"?\\n###Input: \\\"I love pizza\\\", \\\"Pizza is my favorite food\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input: A tensor of strings, output: A tensor of shape (num_examples, 512)\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I love machine learning.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'SNLI, MultiNLI, SICK\\', \\'accuracy\\': \\'86.3%, 76.6%, 88.0%\\'}, \\'description\\': \\'A pre-trained model for encoding text into a 512-dimensional vector. Useful for various NLP tasks like semantic similarity, sentiment analysis, and text classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 90, "text": " My friend sent me a picture of an animal and I want to find out the type of animal in the picture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 91, "text": " Summarize the article and identify the key topics discussed.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image pose detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detects human poses in images\\', \\'api_name\\': \\'movenet\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\", \\'api_arguments\\': \\'image_tensor\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\\\\nimage_tensor = ... # Load image tensor\\\\noutputs = model(image_tensor)\\\\nkeypoints = outputs[\\'output_0\\'].numpy()\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'70.4 mAP\\'}, \\'description\\': \\'MoveNet is a family of efficient and accurate pose detection models designed for real-time applications. This model detects 17 key points of a single person in an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 92, "text": " Help me identify what's in a photo using a model that is pre-trained on the ImageNet dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': [\\'URL: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API provides a pre-trained MobileNetV2 model for extracting feature vectors from images. The model is trained on the ImageNet dataset and achieves 71.9% accuracy in classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 93, "text": " Translate the following sentence into every possible language and create a high-dimensional vector representation for each translated sentence.\\n###Input: \\\"The current world population is 7.9 billion.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image feature extraction\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False)\\\\n])\\\\n\\\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\\\nfeatures = model(x)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\\'}', metadata={})]", "category": "generic"} {"question_id": 94, "text": " I am trying to find objects in an image with high accuracy. Help me use a pre-trained model to detect objects.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'import tensorflow as tf\\', \\'import tensorflow_hub as hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\nloaded_model = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image_path\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = image / 255.0\\\\nimage = tf.expand_dims(image, 0)\\\\n\\\\noutput = loaded_model(image)\", \\'performance\\': {\\'dataset\\': \\'\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'Load a pre-trained TensorFlow Hub model for image object detection and use it to detect objects in an input image.\\'}', metadata={})]", "category": "generic"} {"question_id": 95, "text": " I have a fashion catalog and I want to find visually similar clothes from the catalog using the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 96, "text": " I want to identify what objects are in the image I took on my vacation. I want a lightweight and fast model for this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 97, "text": " Could you identify the object in an image? Which pre-trained model can I use?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 98, "text": " Find out what an image is about by using a pre-trained image classification model, considering its contents.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'This API provides pre-trained image classification models from TensorFlow Hub. Users can load a model using its model_id and make predictions on images.\\'}', metadata={})]", "category": "generic"} {"question_id": 99, "text": " Develop a model for product categories from images I provide.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"tf.keras.Sequential([hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')])\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'(224, 224, 3)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'2.x\\', \\'tensorflow_hub\\': \\'0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=(224,224,3))])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNet V2 architecture from TensorFlow Hub. It is capable of classifying images into 1000 categories with 71% accuracy on the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 100, "text": " Can you please provide an API that will help us find semantic similarity between multiple sentences?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 101, "text": " Describe an object in an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\ndetections = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained object detection model that can detect multiple objects in an image using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 102, "text": " I have a collection of paintings and I'm looking for a way to generate a list of their most common features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 103, "text": " We have new images containing scenes from different locations. Let's classify these locations by implementing a pre-trained image classification model based on MobileNet V2.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification model\\', \\'api_name\\': \\'imagenet-mobilenet_v2_130_224-classification\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\', \\'output_shape\\': \\'[1001]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\n\\\\npredictions = model.predict(input_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.856\\'}, \\'description\\': \\'This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 104, "text": " Can you help extract meaning from a list of sentences by converting them into high-dimensional vectors?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text preprocessing\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'text_input\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'0.78\\'}, \\'description\\': \\'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\\'}', metadata={})]", "category": "generic"} {"question_id": 105, "text": " We have a list of YouTube video descriptions, and we want to find similar videos based on the descriptions, so start with getting the embeddings of each description.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Video classification model\\', \\'api_name\\': \\'Video_classification_model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/Video_classification_model_id/1\\')\", \\'api_arguments\\': [], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'Kinetics-400\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\\'}', metadata={})]", "category": "generic"} {"question_id": 106, "text": " I want a tool to help me count how many cars are parked in our parking lot.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 107, "text": " Can you help me classify the species of a bird that I have in my photo? \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 108, "text": " Can you help me analyze the contents of a photograph?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 109, "text": " I have some random objects lying around my home office desk. Could you please create a model that can classify these objects for me?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 110, "text": " Analyze various photograps and determine what objects each one contains.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image feature extraction\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False)\\\\n])\\\\n\\\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\\\nfeatures = model(x)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\\'}', metadata={})]", "category": "generic"} {"question_id": 111, "text": " Determine the type of animal we found during the hike. It's important to know if it could be dangerous.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 112, "text": " Expedia wants to recommend hotels based on the image of their rooms. They need a way to extract features from these images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[batch_size, 224, 224, 3]\\', \\'output_shape\\': \\'[batch_size, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3),\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model that can be used to extract features from images. The model is based on the MobileNetV2 architecture and is trained on the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 113, "text": " Automate the process of training a deep learning model to distinguish between images of cats and dogs.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 114, "text": " We need to detect broken objects in an assembly line from images. Suggest an appropriate API call that can help us generate feature vectors for this purpose.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 115, "text": " I need a model that can classify different architectural styles in images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification model\\', \\'api_name\\': \\'imagenet-mobilenet_v2_130_224-classification\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\', \\'output_shape\\': \\'[1001]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\n\\\\npredictions = model.predict(input_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.856\\'}, \\'description\\': \\'This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 116, "text": " Provide me a pre-trained model to classify imported images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 117, "text": " I'd like to automatically classify food images at a grocery store to add labels to the products.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 118, "text": " Analyze a collection of images to help me recognize my dog's breed.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 224, 224, 3])\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Varies depending on the model\\'}, \\'description\\': \\'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 119, "text": " Create a feature vector from an image using a pre-trained model for use in an image similarity search.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 120, "text": " I have an app that allows users to take pictures of objects with their phones, and I want to know what those objects are.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 121, "text": " What is the similarity between two sentences? First encode them as vectors, and then calculate the cosine similarity.\\n###Input: {\\\"sentence1\\\": \\\"The quick brown fox jumps over the lazy dog.\\\", \\\"sentence2\\\": \\\"A sly red vixen quickly leaps above the dozing hound.\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 122, "text": " My CEO sent me an email, and he is asking me to analyze the emails' tone to see whether it is positive or negative. We need to get an embedding for this email before classifying the sentiment.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 123, "text": " We need recommendations for similar restaurants. Calculate the embedding of restaurant descriptions to create a list of the most similar establishments.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 124, "text": " Convert a book summary into a high-dimensional vector for determining similarity to another book's summary.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 125, "text": " Tell me how to convert post titles into numerical vectors so that I can find similar titles later on.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text using pre-trained models\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'85.1%\\'}, \\'description\\': \\'A pre-trained text embedding model that can be used to convert text into high-dimensional vectors for various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 126, "text": " We want to analyze security camera footage and determine if there are any people in the frame.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 127, "text": " I took a picture of my park today. I want to know what kinds of objects I captured in the picture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 128, "text": " Our application needs to identify objects in an image. Extract them using pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\\')\", \\'api_arguments\\': {\\'image\\': \\'Input image tensor\\', \\'num_detections\\': \\'Number of detected objects\\', \\'detection_boxes\\': \\'Bounding boxes for detected objects\\', \\'detection_classes\\': \\'Class labels for detected objects\\', \\'detection_scores\\': \\'Confidence scores for detected objects\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\\')\\\\n\\\\n# Preprocess image\\\\ninput_image = tf.image.resize(image, (640, 640))\\\\ninput_image = tf.expand_dims(input_image, axis=0)\\\\n\\\\n# Run detector\\\\noutput = detector(input_image)\\\\n\\\\n# Extract results\\\\nnum_detections = output[\\'num_detections\\'].numpy()\\\\ndetection_boxes = output[\\'detection_boxes\\'].numpy()\\\\ndetection_classes = output[\\'detection_classes\\'].numpy()\\\\ndetection_scores = output[\\'detection_scores\\'].numpy()\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP 0.32\\'}, \\'description\\': \\'A pre-trained object detection model based on the SSD Mobilenet V2 architecture, capable of detecting multiple objects in an image. The model is trained on the COCO dataset and achieves a mean Average Precision (mAP) of 0.32.\\'}', metadata={})]", "category": "generic"} {"question_id": 129, "text": " Compare the similarity of two customer reviews for a product and see if they are positive or negative.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 130, "text": " I took a photo of my backyard and want to find similar images online. Please extract features from the photo so I can search for similar photos.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 131, "text": " I want to compare the meaning similarity of two sentences. Find a way to encode the sentences and calculate their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 132, "text": " My retail client wishes to recognize when a doorbell is rang in audio files of their store.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio Speech-to-Text\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Speech Recognition\\', \\'api_name\\': \\'wav2vec2_large_960h\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\\')\", \\'api_arguments\\': \\'\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'LibriSpeech\\', \\'accuracy\\': \\'0.960\\'}, \\'description\\': \\'A pre-trained model for converting spoken language in audio files into written text using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 133, "text": " For a building project, we need to analyze pictures of different rocks and determine their structures. Obtain the feature vector of a rock image first.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 134, "text": " I need to know what is in this photo I took. Can you tell me?\\n###Input: {\\\"image\\\": \\\"path/to/image.jpg\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 135, "text": " Identify objects in an image to let me know what objects are in the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 136, "text": " My friend sent me an image of an animal, and I am not sure what kind of animal it is. Can you tell me what animal is in the image?\\n###Input: \\\"\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load image and preprocess\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\\\n\\\\n# Make prediction\\\\npredictions = model(tf.expand_dims(image, 0))\\\\n\\\\n# Decode predictions\\\\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy(), top=5)\\\\nfor class_name in class_names[0]:\\\\n print(class_name)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows you to load a pre-trained Inception V3 model from TensorFlow Hub for image classification tasks. The model is trained on the ImageNet dataset and achieves an accuracy of 77.9%.\\'}', metadata={})]", "category": "generic"} {"question_id": 137, "text": " For an image classification task, we need a model capable of recognizing and differentiating between various object types. Please load a pre-trained model for this purpose.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load image and preprocess\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\\\n\\\\n# Make prediction\\\\npredictions = model(tf.expand_dims(image, 0))\\\\n\\\\n# Decode predictions\\\\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy(), top=5)\\\\nfor class_name in class_names[0]:\\\\n print(class_name)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows you to load a pre-trained Inception V3 model from TensorFlow Hub for image classification tasks. The model is trained on the ImageNet dataset and achieves an accuracy of 77.9%.\\'}', metadata={})]", "category": "generic"} {"question_id": 138, "text": " In order to analyze customer feedback from my online store, I need a way to convert their feedback into a numeric format that can be processed by my algorithms.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 139, "text": " I'm working on an app for smart parking stations, and I would like to detect and recognize the car plates from our CCTV footages.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.7.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3), trainable=False)\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\n\\\\n# Get the feature vector\\\\nfeatures = model.predict(input_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset. It can be used to extract features from images for various tasks like image classification, object detection, and image similarity.\\'}', metadata={})]", "category": "generic"} {"question_id": 140, "text": " We need to build a car and bike image classifier. First, we need to extract features from the images to use for training the model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 141, "text": " I want to compare the semantic similarity between two sentences. Give me the embeddings using pre-trained models.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 142, "text": " Sniff out the odd one among the following sentences: \\\"The party was amazing.\\\", \\\"Call a doctor.\\\", \\\"The conference was well organized.\\\"\\n###Input: ['The party was amazing.', 'Call a doctor.', 'The conference was well organized.']\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id: The ID of the pre-trained model to be used for image classification\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\\\n\\\\n# Make a prediction\\\\npredictions = model(tf.expand_dims(image, 0))\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows users to perform image classification using a pre-trained TensorFlow Hub model. The model can be loaded using the model_id, and the API provides functionality for loading and preprocessing images, as well as making predictions.\\'}', metadata={})]", "category": "generic"} {"question_id": 143, "text": " We want to recommend similar fashion items based on a user's uploaded image. Extract image features and compare them with our fashion database.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 144, "text": " When creating a mobile application that uses image recognition, it should be able to determine what items are in the photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 145, "text": " I have some images and I want to compare them efficiently. Extract the features of the images so I can perform similarity analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 146, "text": " Manage the camera in the smartphone to recognize multiple objects during a forest walk.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'Model ID\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'The model ID of the TensorFlow Hub object detection model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_tensor = tf.convert_to_tensor(image_array)\\\\ninput_tensor = input_tensor[tf.newaxis, ...]\\\\n\\\\ndetections = detector(input_tensor)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO2017\\', \\'accuracy\\': \\'mAP@0.5IOU = 0.244\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub object detection model that can detect multiple objects in an image, based on the SSD MobileNet V2 architecture.\\'}', metadata={})]", "category": "generic"} {"question_id": 147, "text": " A tourist application wants to give real-time object recognition to help tourists when they take a picture of an object in the city.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use an object detection model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'The id of the model to be loaded from TensorFlow Hub\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\ndetected_objects = model(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Unknown\\'}, \\'description\\': \\'Load and use an object detection model from TensorFlow Hub for detecting objects in images.\\'}', metadata={})]", "category": "generic"} {"question_id": 148, "text": " We want to help users identify the species of a bird in a given picture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 149, "text": " I want to analyze a list of customer reviews to gain insight into their sentiment.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 150, "text": " Gather high-dimensional embeddings of some scientific abstracts to make a similarity-based network analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional space\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\", \\'performance\\': {\\'dataset\\': \\'various\\', \\'accuracy\\': \\'not specified\\'}, \\'description\\': \\'A pre-trained model that encodes text into 512-dimensional embeddings using a deep averaging network (DAN). Useful for a variety of natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 151, "text": " Use a pre-trained model to find out traits that are similar to buildings in images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 152, "text": " I need to measure the semantic similarity between pairs of sentences. Help me to get the embeddings of the sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 153, "text": " I work in a post office, and need to classify bills and documents from a mixed package. Please extract image features so that we can use these features for classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 154, "text": " We are building an AI app that can help us determine what kind of food is in a picture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image pose detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detects human poses in images\\', \\'api_name\\': \\'movenet\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\", \\'api_arguments\\': \\'image_tensor\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\\\\nimage_tensor = ... # Load image tensor\\\\noutputs = model(image_tensor)\\\\nkeypoints = outputs[\\'output_0\\'].numpy()\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'70.4 mAP\\'}, \\'description\\': \\'MoveNet is a family of efficient and accurate pose detection models designed for real-time applications. This model detects 17 key points of a single person in an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 155, "text": " Categorize images captured from a drone in different landscapes like forests, rivers, and mountains.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract high-level features from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3), trainable=False),\\\\n tf.keras.layers.Dense(1, activation=\\'sigmoid\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=\\'adam\\', loss=\\'binary_crossentropy\\', metrics=[\\'accuracy\\'])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It extracts high-level features from images and can be used for tasks like image classification, object detection, and transfer learning.\\'}', metadata={})]", "category": "generic"} {"question_id": 156, "text": " I am planning to sort and label my photo collection. Help me identify different objects in my master album.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'faster_rcnn_resnet50_v1_640x640\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\\')\\\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image_tensor[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU: 37.1%\\'}, \\'description\\': \\'A pre-trained Faster R-CNN model with ResNet-50 as the backbone for object detection in images. The model is trained on the COCO dataset and can detect objects from 80 different classes.\\'}', metadata={})]", "category": "generic"} {"question_id": 157, "text": " Determine the dominant object in an image by extracting features from the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 158, "text": " Estimate the similarity between two news headlines using pre-trained text embedding.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 159, "text": " I want to build a simple mobile app that identifies plants by analyzing photos of their leaves.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 160, "text": " I want to make a food identification app that can help me classify different dishes at a restaurant.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \\'tf.keras.Sequential([hub.KerasLayer(model_id)])\\', \\'api_arguments\\': [\\'model_id: The identifier of the pre-trained model to load\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/1\\'\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\\\\n\\\\n# Load an image and make a prediction\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\npredictions = model.predict(input_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'A pre-trained image classification model using the Inception V3 architecture, available on TensorFlow Hub. The model can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 161, "text": " I want to know what is in this image I just took.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 162, "text": " We need to classify images taken by our surveillance camera to detect unwanted activity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection and classification in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 163, "text": " We are working with an autonomous vehicle company, and they want to detect and classify objects in the environment while the vehicle is on the road.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection and classification in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 164, "text": " How do I know the breed of a random dog in a picture?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Semantic segmentation\\', \\'api_name\\': \\'deeplabv3\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3/1\\')\", \\'api_arguments\\': [\\'input_tensor\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nsegmentation = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3/1\\')\\\\ninput_tensor = tf.random.uniform(shape=[1, 1024, 1024, 3], dtype=tf.float32)\\\\noutput = segmentation(input_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'87.5\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art semantic segmentation model that uses atrous convolution and fully connected conditional random fields for dense prediction. It can be used for various image segmentation tasks, such as object segmentation, scene parsing, and person segmentation.\\'}', metadata={})]", "category": "generic"} {"question_id": 165, "text": " I have a picture of my room and I want to identify all the objects present in it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 166, "text": " I have a collection of movie descriptions and I want to find the resemblance between them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 167, "text": " To find similar images in a database, we need to extract useful features to compare images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 168, "text": " Which API could be used to compare the semantic similarity between two given pieces of text?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 169, "text": " We have some cars pictures and want to classify them, give us the API call to get a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 170, "text": " We have received customer complaints about our products and want to group them into different categories to address them more effectively. Help us get embeddings for the complaint text data to enable clustering.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 171, "text": " Help me convert Reddit post comments into numerical vector format that can be easily understood by machine algorithms.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 172, "text": " Organize a digital library of photos and identify the ones with similar content by comparing their image features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 173, "text": " Given a collection of images, find the most likely object depicted in each image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the ID of the model to be loaded\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Run the detector on an image\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Varies depending on the model version\\'}, \\'description\\': \\'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 174, "text": " I am making an image recognition app, and I want to extract features from each image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 175, "text": " Analyze whether the descriptions of two movies are similar by generating dense vector representations of the text and comparing them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 176, "text": " We have a set of images that we want to classify into different categories. We need a pre-trained image classification model for this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'IMAGE_SHAPE+(3,)\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nIMAGE_SHAPE = (224, 224)\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=IMAGE_SHAPE+(3,))\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=IMAGE_SHAPE)\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nresult = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 177, "text": " Our team has an image classification project. First, we need to extract features from our images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 178, "text": " I want to cluster customer chat messages so that their related concerns are addressed in a more meaningful way.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \\'import tensorflow_hub as hub\\\\nmodel = hub.load(\"https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\")\\\\nsegmentation = model(input_image)\\', \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.897\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\\'}', metadata={})]", "category": "generic"} {"question_id": 179, "text": " I want to build a text classifier for articles that could also find articles with similar content. Can you suggest a way to encode the text of the articles?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 180, "text": " There is a need to segment images of a park area with trees and people, and distinguish the trees from people.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 181, "text": " Our security firm needs an image analysis solution to detect cars and pedestrians from CCTV images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 182, "text": " Can you recommend an API that helps to convert a research article abstract into a high-dimensional vector which we can then use to find similar articles?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\'\\\\nmodel = hub.load(model_id)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to load a pre-trained image classification model from TensorFlow Hub using a specific model_id. The model can then be used to classify images into different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 183, "text": " I need to monitor a warehouse's inventory and identify objects kept in the storage area.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 184, "text": " Create a feature extraction model to recognize 10 different types of images from a given dataset with sizes 224x224x3.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 185, "text": " I need a quick way to classify images on my phone. Can you use a computationally efficient model to do this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 186, "text": " Provide me with the facility to determine the similarity between two sentences using encoded text.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 187, "text": " We want to recognize different types of dogs in images for an upcoming app. Help us with a model to get the image features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 188, "text": " I am planning my garden, please help me segment plants in an image for better landscape planning.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nimage = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in an image using the Single Shot MultiBox Detector (SSD) with MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 189, "text": " Find out the type of birds in my backyard based on smartphone photos I took.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow>=2.0\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\n\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'0.545 mAP\\'}, \\'description\\': \\'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\\'}', metadata={})]", "category": "generic"} {"question_id": 190, "text": " I'm working on a project that requires text clustering, and I need to convert a list of product reviews into vector representations in order to analyze the semantic similarities between them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text: A list of strings to be embedded\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STSbenchmark\\', \\'accuracy\\': \\'Pearson correlation of 0.803\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into a high-dimensional vector that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 191, "text": " I found a bird on my trip outside, and I took a photo. Can you identify it?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate image feature vectors\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'output_shape\\': \\'[1280]\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow>=2.0.0\\', \\'tensorflow-hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n output_shape=[1280],\\\\n trainable=False),\\\\n tf.keras.layers.Dense(5, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset. Can be used for transfer learning and generating image feature vectors.\\'}', metadata={})]", "category": "generic"} {"question_id": 192, "text": " Figure out the category of my image by using a pre-trained Inception model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nloaded_model = hub.load(model_id)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load an image classification model from TensorFlow Hub by providing the model_id. This example demonstrates how to load the Inception V3 model.\\'}', metadata={})]", "category": "generic"} {"question_id": 193, "text": " Images have come from users, and we need to recognize their categories for better curation.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\", \\'api_arguments\\': \\'image, score_threshold\\', \\'python_environment_requirements\\': \\'Python 3, TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\\\\nresult = detector(image, score_threshold=0.5)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'mAP@0.5IOU = 32.8%\\'}, \\'description\\': \\'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 194, "text": " We have several support tickets in our system, find a way to cluster them to find the most occurring topics.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 195, "text": " We need to create an image classifier using transfer learning. I want to get feature vectors from pre-trained models.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 196, "text": " Can you help me to create fixed-sized vector representation for every input phrases in a dataframe?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 197, "text": " I need an API to help me classify pet images. I'd like to use the extracted features from these images to make better predictions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 198, "text": " Help me with identifying the object present in my new picture, using a ready-to-use model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Identify and locate objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'input\\': \\'Tensor of shape [batch_size, height, width, 3]\\', \\'output\\': \"Dictionaries with keys \\'detection_boxes\\', \\'detection_classes\\', \\'detection_scores\\'\"}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\\\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP of 0.22\\'}, \\'description\\': \\'A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects in images.\\'}', metadata={})]", "category": "generic"} {"question_id": 199, "text": " I want to analyze images of my coin collection and build a classifier to identify coins. Provide me with a method to extract image features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 200, "text": " We are doing data pre-processing for a project and our aim is to understand if the given two product titles are semantically similar or not. We first need to convert these product titles into high-dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 201, "text": " I'm working on a plant recognition app, and I want to classify a plant's image taken by users.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 202, "text": " How can I calculate the similarity between two sentences based on their embeddings?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 203, "text": " I have an image of my backyard. Identify if the BBQ grill is active.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 204, "text": " I am building a phone app that recognizes food. We need a lightweight model to identify different types of food.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'IMAGE_SHAPE+(3,)\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nIMAGE_SHAPE = (224, 224)\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=IMAGE_SHAPE+(3,))\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=IMAGE_SHAPE)\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nresult = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 205, "text": " We are interesting in analyzing text messages to recommend interesting articles to our users. For this, we need to create an embedding for each message.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Create image feature vector\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\'\\\\nmodel = hub.load(model_id)\\\\nfeature_vector = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\\'}', metadata={})]", "category": "generic"} {"question_id": 206, "text": " Could you help me to find a quick way to rate similarities between my blog posts?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 207, "text": " I have a lot of vacation pictures and want to find those with similar content. Can you help me?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 208, "text": " I am doing holiday gift shopping online, and I want to know what category a picture of a gift item belongs to.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 209, "text": " Can you find an efficient image classification model to use on my smartphone for recognizing food items in my fridge?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL of the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Depends on the specific model\\'}, \\'description\\': \\'This API allows you to load a pre-trained image classification model from TensorFlow Hub for use in your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 210, "text": " Our art gallery is conducting an exhibition and we want to classify the images of paintings into different genres.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 211, "text": " We are setting up an AI system to help classify images from outdoor events into different categories based on their content.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset. It can classify images into 1000 different categories with an accuracy of 71.0%.\\'}', metadata={})]", "category": "generic"} {"question_id": 212, "text": " I am creating a shopping app and I want to suggest similar looking items from our database using item's image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 213, "text": " I have an image and I want to recognize the object in this image, so I need to access a pre-trained image classification model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 214, "text": " I have taken some pictures of my dog, and I want to organize them in a way that those which are similar to each other are together.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 215, "text": " We want to find the most similar questions from user inputs to give them the best answer.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 216, "text": " I am working on a product recommendation system for a flower shop. Extract feature vectors from flower images to be used in the system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracting feature vectors from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3))\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Extract the feature vector\\\\nfeature_vector = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to extract feature vectors from images using the MobileNetV2 model trained on the ImageNet dataset. The feature vectors can be used for various machine learning tasks, such as image classification, image retrieval, or transfer learning.\\'}', metadata={})]", "category": "generic"} {"question_id": 217, "text": " Recommend a playlist to me based on the audio characteristics of this song clip I'm sending you.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Audio feature extraction\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': [{\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/yamnet/1\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load a wav file\\\\nfilename = \\'audio_file.wav\\'\\\\nfile_contents = tf.io.read_file(filename)\\\\nwaveform = tf.audio.decode_wav(file_contents).audio\\\\n\\\\n# Extract features\\\\nscores, embeddings, spectrogram = model(waveform)\", \\'performance\\': {\\'dataset\\': \\'AudioSet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \"YAMNet is a deep net that predicts 521 audio event classes based on the AudioSet dataset. It\\'s used for audio feature extraction and can be used for tasks like audio classification, tagging, and recommendation.\"}', metadata={})]", "category": "generic"} {"question_id": 218, "text": " We are building a car recognition system. We need to extract features from car images to feed them into the classifier.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 219, "text": " Please help me with a system that studies product descriptions so that we can group products with similar features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 220, "text": " I need to find features in the images to perform image classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 221, "text": " We need to find similar news articles from a large corpus. Please embed them into high-dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 222, "text": " Discover all the elements present within a photograph of a street scene.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The unique identifier of the model to be loaded from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\nresult = detector(image[np.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'Open Images V4\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Loads a TensorFlow Hub model for image object detection, allowing the detection of objects within images using the Faster R-CNN architecture with Inception ResNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 223, "text": " We own a car dealer business and want to develop an app that categorizes different car categories based on uploaded images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and run a pre-trained image classification model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Run the model on a sample image\\\\nsample_image = tf.zeros([1, 224, 224, 3])\\\\nlogits = model(sample_image)\\\\npredicted_class = tf.argmax(logits, axis=-1)\\\\nprint(predicted_class)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows you to load and run a pre-trained MobileNetV2 image classification model from TensorFlow Hub. The model is trained on the ImageNet dataset and can classify images into 1000 different categories with an accuracy of 71.9%.\\'}', metadata={})]", "category": "generic"} {"question_id": 224, "text": " Given images of painted landscapes, we want to find similar images based on their visual features. Which API can we use for extracting the feature vector?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 225, "text": " We need to know what is in these photos so we can report to our officials.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 226, "text": " We have a collection of animal photos, and we want to obtain a feature vector from each photo in order to properly organize them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 227, "text": " I took a picture of a street and want to separate the vehicles, people, and buildings in the scene.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 228, "text": " I need to quickly classify images of various objects on my phone. Which pre-trained model should I use for efficient processing in a mobile environment?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text for various natural language processing tasks\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'text_input\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'Embedding with Universal Sentence Encoder.\\'])\", \\'performance\\': {\\'dataset\\': \\'STS-Benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803\\'}, \\'description\\': \\'A text embedding model that converts text into high-dimensional vectors for use in natural language processing tasks. The Universal Sentence Encoder is trained on a variety of data sources and can be fine-tuned for specific tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 229, "text": " I want to enhance my wildlife images and need to extract an image feature vector to use in advanced image processing tasks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 230, "text": " My computer has folders with many images, and I want to classify these images into categories, such as animals, buildings, and so on.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection and classification in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 231, "text": " Show me how to classify an image of a dog using a pre-trained model with TensorFlow Hub and Inception V3 architecture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\npredictions = model(input_image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'TensorFlow Hub provides pre-trained models for image classification tasks. This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 232, "text": " Recommend a suitable movie based on a given movie's plot. For better recommendations, obtain the plot's embedding.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 233, "text": " Obtain image features from an image dataset to use in a product recommendation system\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 234, "text": " I took a photo of a car and I want to extract its features in a vector form to use it for classification tasks later.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 235, "text": " Calculate the similarity between two sentences and determine if they have the same meaning.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 236, "text": " We have an image dataset and want a pre-trained model as a feature extractor.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction\\', \\'api_name\\': \\'imagenet_mobilenet_v1_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/4\\')\", \\'api_arguments\\': \\'input_shape, batch_size, dtype, trainable\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/4\\', input_shape=(224, 224, 3), trainable=False),\\\\n tf.keras.layers.Dense(1, activation=\\'sigmoid\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'70.9%\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV1 architecture for image classification and feature extraction. The model is trained on the ImageNet dataset and provides an accuracy of 70.9%.\\'}', metadata={})]", "category": "generic"} {"question_id": 237, "text": " We are creating a custom image search engine that can give similar image results. Extract features from images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3])\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, 0)\\\\n\\\\n# Extract features from the image\\\\nfeatures = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A pre-trained deep learning model that extracts features from images using the MobileNetV2 architecture.\\'}', metadata={})]", "category": "generic"} {"question_id": 238, "text": " Can you compare two sentences similarly using the embedding generation?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 239, "text": " Provide me with an encoded version of the following review, so I can later use it for sentiment analysis: \\\"What an amazing product! I've been using it for a few weeks now and couldn't be happier.\\\"\\n###Input: \\\"What an amazing product! I've been using it for a few weeks now and couldn't be happier.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\npredictions = model(input_image)\\\\npredicted_class = tf.argmax(predictions[0]).numpy()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image. The model has been trained on the ImageNet dataset and achieves 78.1% accuracy.\\'}', metadata={})]", "category": "generic"} {"question_id": 240, "text": " I have several images of various objects; can you check if there are any boats in them?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 241, "text": " We are building a software that can recognize similar objects in a series of images. We need feature vectors for each image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 242, "text": " Can you help me obtain the embeddings of a list of sentences for a research on similarity between news headlines?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 243, "text": " I have an image dataset and I want to compute feature vectors to perform similarity computation using nearest neighbor searching.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 244, "text": " A dating website wants to auto-match users based on the similarity of their profiles. How can we do that using text embeddings?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 245, "text": " We are creating a mobile app where users can take photos of objects, and our app should identify what the object is.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 246, "text": " We are building a Tinder-clone app for pets adoption. When user swipes, we want to show them the most similar pets based on images. Therefore, in order to find similar pets, we need to get the feature vector of each pet photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 247, "text": " I have pictures on my phone and want to organize them into categories. Help me identify what each picture represents.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 248, "text": " Identify the category for these images to decide which category I should tag them on my blog.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[batch_size, 224, 224, 3]\\', \\'output_shape\\': \\'[batch_size, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,),\\\\n trainable=False)\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API provides a feature vector for images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have shape (224, 224, 3), and the output feature vector has 1280 dimensions. The model is available on TensorFlow Hub and can be used for transfer learning applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 249, "text": " Provide an API call that can detect any landmark in a given photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 250, "text": " We have a car selling website and want to identify the model of the car based on the images uploaded by the seller.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 251, "text": " I'd like to look into a list of articles and rank them by their similarity to the provided statement. \\n###Input: \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 252, "text": " Can you suggest how to extract features from animal images and classify them into their respective species?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracts feature vectors from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nfeatures = model.predict(image)\\\\nprint(features)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This API provides a pre-trained Inception V3 model for extracting feature vectors from images. It is built on TensorFlow Hub and requires TensorFlow and TensorFlow Hub packages. The example code demonstrates how to load an image, preprocess it, and extract features using the API.\\'}', metadata={})]", "category": "generic"} {"question_id": 253, "text": " Create an image-based recommendation system to identify visually similar products.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 254, "text": " Our company wants to create a product recommender that sorts products based on visual similarity, help us identify the products in images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 255, "text": " I have a list of short movie descriptions and want to get an embedding of each description.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 256, "text": " Develop an application that can recognize the names of items on a store shelf based on their images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Video classification model\\', \\'api_name\\': \\'Video_classification_model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/Video_classification_model_id/1\\')\", \\'api_arguments\\': [], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'Kinetics-400\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\\'}', metadata={})]", "category": "generic"} {"question_id": 257, "text": " Determine which politicians are similar to each other based on their quotes by obtaining an embedding for each quote.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 258, "text": " I have a list of movie reviews and I want to find the semantic similarity between them. How can I convert theses reviews into numerical data?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 259, "text": " There are some insects in the park, I want to recognize them using an app.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 260, "text": " I want to inspect my living room and detect all the objects in a photo I have taken.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 261, "text": " A retail store needs a tool that will be able to detect misplaced items on their shelves.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nloaded_model = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage_np = np.array(image)\\\\n\\\\ninput_tensor = tf.convert_to_tensor(image_np)\\\\ninput_tensor = input_tensor[tf.newaxis, ...]\\\\n\\\\noutput_dict = loaded_model(input_tensor)\\\\n\\\\nboxes = output_dict[\\'detection_boxes\\'][0].numpy()\\\\nscores = output_dict[\\'detection_scores\\'][0].numpy()\\\\nclasses = output_dict[\\'detection_classes\\'][0].numpy().astype(np.int32)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 262, "text": " I need to build a pet breed classifier from images. I want to use a pre-trained model to extract feature vectors of a pet image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 263, "text": " We have set of images and want classify them into meaningful categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 264, "text": " Show me how to get the semantic similarity between two sentences.\\n###Input: \\\"I am going to the store to buy groceries.\\\", \\\"I will visit the market to purchase food items.\\\" \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 265, "text": " I took a photo of my friends playing soccer. I am making a collage and we need to remove the background from the photo, segmenting the players in the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id: The ID of the pre-trained model to be used for image classification\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\\\n\\\\n# Make a prediction\\\\npredictions = model(tf.expand_dims(image, 0))\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows users to perform image classification using a pre-trained TensorFlow Hub model. The model can be loaded using the model_id, and the API provides functionality for loading and preprocessing images, as well as making predictions.\\'}', metadata={})]", "category": "generic"} {"question_id": 266, "text": " Investigate art works in a museum by creating a feature vector database for all paintings on display.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 267, "text": " Once a day, I take a photo of my backyard to observe how the plants change over time. Detect what is present in the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the TensorFlow Hub model URL\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Detect objects in the image\\\\nresult = model(image)\\\\n\\\\n# Print detected objects\\\\nfor obj in result[\\'detection_boxes\\']:\\\\n print(obj)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\\'}', metadata={})]", "category": "generic"} {"question_id": 268, "text": " I would like to be able to identify an animal in a photo I took while traveling.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'text_input\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'0.803\\'}, \\'description\\': \\'A text embedding model that maps text inputs to high-dimensional vectors for use in downstream tasks like text classification or clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 269, "text": " In order to classify animals, I need to extract image features to train my own classification model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 270, "text": " What kind of furniture is in my room? I want to take a photo and recognize which type of furniture it is.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image_path\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture, which can recognize 1000 different classes from the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 271, "text": " Create a mobile application that recognizes dogs and cats. I need features from photos of dogs and cats.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 272, "text": " Could you calculate the similarity between the sentences \\\"I have a cat\\\" and \\\"My pet is a kitten\\\"?\\n###Input: [\\\"I have a cat\\\", \\\"My pet is a kitten\\\"]\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 273, "text": " How can I get a descriptive vector from an image to use for a custom classification task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 274, "text": " Tell me which kind of animal is in this image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 275, "text": " Calculate the similarity between two sentences provided by users.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 276, "text": " I need a recommendation for similar items given a product description. First, get the embeddings of product descriptions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 277, "text": " Provide me with a method to convert sentences into numerical vectors for text analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 278, "text": " For our e-commerce product recommendation system, we need to find similar products based on a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': {\\'model_id\\': \\'string\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into various categories. The example provided demonstrates how to load the Inception V3 model and make predictions on a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 279, "text": " I have a dataset of user reviews written in English, and I'd like to prepare it for a sentiment analysis task. Could you help me preprocess the texts for this purpose?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 280, "text": " Create a recommender system that recommends related products based on their descriptions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Video classification model\\', \\'api_name\\': \\'Video_classification_model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/Video_classification_model_id/1\\')\", \\'api_arguments\\': [], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'Kinetics-400\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\\'}', metadata={})]", "category": "generic"} {"question_id": 281, "text": " Analyze a photo from a running marathon to identify the objects in the scene.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 282, "text": " We need to analyze security camera footage and identify any intruding person or object in the frames.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 283, "text": " Need a code snippet to compute similar texts among thousands of sentences based on a similarity score.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 284, "text": " My niece wants to learn the names of different birds so we can play a game where she sees a bird picture and the app provides a guess about the bird's species.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 285, "text": " Can you help me recognize a flower from a picture of it?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 286, "text": " I want to search through our art collection and find similar artworks. Create a model to extract features from the artwork images that can be used for comparison and clustering.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 287, "text": " I want to build a relationship between different sentences. Can you suggest a method to get a fixed-length vector representation of each sentence?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 288, "text": " I just took a picture of my room, but I want to understand what objects are in it. Help me to analyze the content of the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 289, "text": " I have tons of TikTok videos on my phone and want an AI to categorize them based on actions happening in them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 290, "text": " I would like to use the features extracted from pictures of different outfits for clustering. \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 291, "text": " Analyze the background noise of an audio file and provide the corresponding embeddings to help classify the type of environment it was recorded in.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio event classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Classify audio events\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/yamnet/1\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load audio file and get waveform\\\\naudio_file = \\'path/to/audio/file.wav\\'\\\\nwaveform, sample_rate = tf.audio.decode_wav(tf.io.read_file(audio_file), desired_channels=1)\\\\n\\\\n# Run the model on the waveform\\\\nclass_scores, embeddings, spectrogram = model(waveform)\\\\n\\\\n# Get the top class\\\\ntop_class = tf.argmax(class_scores, axis=-1)\\\\n\\\\n# Print the top class\\\\nprint(\\'Top class:\\', top_class.numpy())\", \\'performance\\': {\\'dataset\\': \\'AudioSet\\', \\'accuracy\\': \\'0.745 mAP\\'}, \\'description\\': \"YAMNet is a pretrained deep net that predicts 521 audio event classes based on the AudioSet dataset. It\\'s useful for audio event classification tasks.\"}', metadata={})]", "category": "generic"} {"question_id": 292, "text": " We built a mobile app for people to identify dogs' breeds. Let's build a model to extract features from dog images and classify them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracts feature vectors from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nfeatures = model.predict(image)\\\\nprint(features)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This API provides a pre-trained Inception V3 model for extracting feature vectors from images. It is built on TensorFlow Hub and requires TensorFlow and TensorFlow Hub packages. The example code demonstrates how to load an image, preprocess it, and extract features using the API.\\'}', metadata={})]", "category": "generic"} {"question_id": 293, "text": " We have a group of 10 people discussing different topics. Analyze their texts and find the most similar pair of text.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 294, "text": " Can you help me classify the contents of an image I took during my vacation?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 295, "text": " I have a list of headlines and would like to find which ones are semantically similar. Can you help me get the high-dimensional vectors for my headlines?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 296, "text": " Suggest a way to compute the similarity between two sentences using embeddings to help us identify which sentences are discussing the same topic.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 297, "text": " Can you help me inidentifying content of some photos from recent vacation?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 298, "text": " My cousin has created an album of zoo animals he captured, and I want to classify the animals using a pre-trained TensorFlow model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\npredictions = model(input_image)\\\\npredicted_class = tf.argmax(predictions[0]).numpy()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image. The model has been trained on the ImageNet dataset and achieves 78.1% accuracy.\\'}', metadata={})]", "category": "generic"} {"question_id": 299, "text": " We want to create a Q&A system, so we need to be able to find the similarity between the questions that website visitors have asked.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 300, "text": " I have a couple of videos in my library, can you suggest a model to classify them based on their content?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Video classification model\\', \\'api_name\\': \\'Video_classification_model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/Video_classification_model_id/1\\')\", \\'api_arguments\\': [], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \\'\\', \\'performance\\': {\\'dataset\\': \\'Kinetics-400\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\\'}', metadata={})]", "category": "generic"} {"question_id": 301, "text": " A political analyst is preparing a report and needs to identify different political ideologies present in the statements made by politicians. Can you provide the embeddings of those statements to create a clustering algorithm?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 302, "text": " My company is developing a furniture recommendation system, and we need to extract features from furniture images to find similar items.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 303, "text": " I am creating a search engine that checks for similar images for a given image. I want to extract the features from images for creating an image search algorithm.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 304, "text": " I have a folder with a lot of images, and I want to cluster them by similarity but I do not know how to represent an image in a consistent way.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nloaded_model = hub.load(model_id)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load an image classification model from TensorFlow Hub by providing the model_id. This example demonstrates how to load the Inception V3 model.\\'}', metadata={})]", "category": "generic"} {"question_id": 305, "text": " I want to recommend similar movies to a user. Therefore, turn movie synopses into vectors with their distances representing similarities.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 306, "text": " I have several photos of objects, and I want to get compact feature vectors for each.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 307, "text": " Can you find out the suitable API call to load pre-trained image feature vector model for classifying images?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 308, "text": " I have a collection of vehicle images, and I want to extract features from the images to help with clustering different types of vehicles based on their appearance.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 309, "text": " I want to know how to get a compact representation of images using MobileNetV1.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\', trainable=False)\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\', trainable=False),\\\\n tf.keras.layers.Dense(1000, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.build([None, 299, 299, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API provides a compact representation of images using the Inception V3 model from TensorFlow Hub. It extracts feature vectors from images, which can be used for various machine learning tasks such as classification and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 310, "text": " Please help me find Latent Semantic Analysis for sentences, and it should convert text into meaningful vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 311, "text": " I am working on a project and need to classify images of objects, such as cars or animals, quickly and efficiently.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'List of sentences\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world\\', \\'I am learning how to use the Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs.\\'}', metadata={})]", "category": "generic"} {"question_id": 312, "text": " Analyze a photo of a car taken during a trip to identify the make and model of the car.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \\'tf.keras.Sequential([hub.KerasLayer(model_id)])\\', \\'api_arguments\\': [\\'model_id: The identifier of the pre-trained model to load\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/1\\'\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\\\\n\\\\n# Load an image and make a prediction\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\npredictions = model.predict(input_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'A pre-trained image classification model using the Inception V3 architecture, available on TensorFlow Hub. The model can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 313, "text": " In a wildlife app, I want to recognize animals from users' images. Fetch the model that can help to classify those images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 314, "text": " Suggest me a method to find semantic similarity between different user queries.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 315, "text": " Calculate the similarity score between two sentences, \\\"I like pizza\\\" and \\\"Pizza is my favorite food.\\\"\\n###Input: {\\\"source_sentence\\\": \\\"I like pizza\\\", \\\"target_sentence\\\": \\\"Pizza is my favorite food\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 316, "text": " I want to identify the contents of various images, can you recommend a pre-trained model for this task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 317, "text": " When eating out or ordering takeout, I would like to know the type of food within 2 seconds by capturing the food image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The unique identifier of the model to be loaded from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\nresult = detector(image[np.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'Open Images V4\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Loads a TensorFlow Hub model for image object detection, allowing the detection of objects within images using the Faster R-CNN architecture with Inception ResNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 318, "text": " Compute the similarity of the two given paragraphs using high-dimensional vectors.\\n###Input: \\\"The quick brown fox jumped over the lazy dog. This sentence contains every letter of the alphabet.\\\", \\\"Pack my box with five dozen liquor jugs. This pangram contains each letter of the English language.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \\'import tensorflow_hub as hub\\\\nmodel = hub.load(\"https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\")\\\\nsegmentation = model(input_image)\\', \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.897\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\\'}', metadata={})]", "category": "generic"} {"question_id": 319, "text": " Tell me which car it is based on its picture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3), trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, which is optimized for mobile and edge devices. It is trained on ImageNet dataset and can be used for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 320, "text": " For a list of sentences, find the sentence closest in meaning to a given phrase.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 321, "text": " Create a simple image classifier that can predict if an object is a cat or a dog.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 322, "text": " I am working on a project about recognizing landmarks and I need to extract image features for comparison purposes.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about embeddings.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various tasks\\', \\'accuracy\\': \\'State-of-the-art\\'}, \\'description\\': \\'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 323, "text": " We want to build a tool to recommend similar art pictures. Please extract features from the image provided.\\n###Input: Example image of an art picture (with RGB values and size 224x224 pixels)\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 324, "text": " What do I need to do if I want to use your image classification model to label categories of animal faces?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 325, "text": " I want to calculate the semantic similarity between job descriptions so I can quickly identify related jobs. Generate embeddings for these texts.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 326, "text": " Recognize and classify objects in an image using a pre-trained model from TensorFlow Hub.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 327, "text": " I am interested in identifying the number of people in a given photograph. Show the best way to detect multiple objects in an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 328, "text": " We have got a CCTV footage of a bird feeding station outside our house. Now we want to count different birds coming at different times during the day.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 329, "text": " My daughter wants to identify butterflies in her butterfly book. I want to use my phone camera to identify them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 330, "text": " I need to analyze customer reviews for various products. Can you provide me with a representation of each review in a high-dimensional vector?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 331, "text": " Suggest a way to compare two images based on their features vectors extracted from a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', trainable=False),\\\\n tf.keras.layers.Dense(10, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API provides a pre-trained image feature vector model based on MobileNetV2 architecture. It extracts features from images and can be used as a base model for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 332, "text": " My clients are dog breeders and they requested a tool that can identify different breeds of dogs automatically.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 333, "text": " Estimate the similarity between two given sentences by embedding them using Universal Sentence Encoder.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 334, "text": " Suppose we have two movie reviews. We would like to know how semantically similar these two movie reviews are.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 335, "text": " How can I compare the similarity between two sentences? I need a numerical representation.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 336, "text": " We need to find the semantic similarity between different product reviews.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 337, "text": " Find the similarities between my favorite quotes from different books so I can group them together.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 338, "text": " I want to provide a semantic search on my website, and I need to compare the similarity between the user query and my database of questions to find the top relevant matches.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 339, "text": " We need to find similarity between different reviews. First, could you create a vector representation for each review?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 340, "text": " Create a customer support ticket classifier to automatically determine the issue category based on the ticket description.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 341, "text": " I have taken images of different items I want to sell on eBay, and I need to extract feature vectors from the images to use in a recommendation system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 342, "text": " Extract a feature vector from an image to perform image classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image feature vectors\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=(224, 224, 3), trainable=False)\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\\\n\\\\n# Extract feature vector\\\\nfeature_vector = model.predict(image[np.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 343, "text": " I have a list of product reviews and I need a program to find the reviews which are most similar to each other.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 344, "text": " After finishing a hike, I want to know which environment I have been to during the hike. My friends provided some pictures of the scenery during the hike.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 345, "text": " I want to construct an automatic tagging system that assigns meaningful tags to content pieces based on the text.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 346, "text": " I want to develop an app to identify specific breeds of dogs from the user's photos of their pets. What kind of model should I use for this task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL of the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Depends on the specific model\\'}, \\'description\\': \\'This API allows you to load a pre-trained image classification model from TensorFlow Hub for use in your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 347, "text": " I have an app that needs to recognize objects in images. We need an image classification model to do that.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 348, "text": " I am working on a movie recommendation system and need a sentence embedding for each movie description.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image feature extraction\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False)\\\\n])\\\\n\\\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\\\nfeatures = model(x)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\\'}', metadata={})]", "category": "generic"} {"question_id": 349, "text": " Identify the primary object in this image and classify it.\\n###Input: Load an image from a given path, `path/to/image.jpg`, with a target size of (224, 224).\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 350, "text": " Analyze an image for any instances of litter to help clean up public spaces.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_130_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\\\\n\\\\n# Prepare an image for input to the model\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Predict the class of the image\\\\npredictions = model(input_image)\\\\npredicted_class_index = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained model for image classification using MobileNetV2 architecture and trained on ImageNet dataset\\'}', metadata={})]", "category": "generic"} {"question_id": 351, "text": " Among a list of sentences, find the most semantically similar pairs by converting them into dense vector representations.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Convert text into a dense vector representation\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'Text or list of texts\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>= 2.0\\', \\'tensorflow_hub\\': \\'>= 0.8\\'}, \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into a dense vector representation that can be used for various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 352, "text": " I have some images of my last trip, and I want to identify the objects in the pictures.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 353, "text": " When I go shopping, I stumble upon an item I can't identify. I want to take a picture and let my phone classify it for me.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 354, "text": " We have an app that allows users to sort their photos by features. Extract features from images for this app.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 355, "text": " My kids found some rare insects, and I need some help identifying them. They took some photos with a smartphone. Can you help classify them?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Identify and locate objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'input\\': \\'Tensor of shape [batch_size, height, width, 3]\\', \\'output\\': \"Dictionaries with keys \\'detection_boxes\\', \\'detection_classes\\', \\'detection_scores\\'\"}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\\\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP of 0.22\\'}, \\'description\\': \\'A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects in images.\\'}', metadata={})]", "category": "generic"} {"question_id": 356, "text": " We need a way to detect phishing email domains. I want to find similarities in domain addresses to do so. Please provide embeddings of email domains.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 357, "text": " I have a review about a new sci-fi book, and I want to generate high-dimensional vectors that are useful for classification tasks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about embeddings.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various tasks\\', \\'accuracy\\': \\'State-of-the-art\\'}, \\'description\\': \\'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 358, "text": " I have a picture of a beach party, and I want to know how many people are there in the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 359, "text": " For document similarity, I want to convert the following sentence into its vector representation: \\\"Artificial Intelligence is transforming the world in ways we couldn't have imagined a decade ago.\\\"\\n###Input: Artificial Intelligence is transforming the world in ways we couldn't have imagined a decade ago.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 360, "text": " Our team needs to keep track of various laptops and their usage in the workplace. Detect the laptops in the given images and provide their locations.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow>=2.0\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\n\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'0.545 mAP\\'}, \\'description\\': \\'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\\'}', metadata={})]", "category": "generic"} {"question_id": 361, "text": " Classify a short clip of people playing football to understand the actions and behaviors of the players.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained model for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [{\\'model_id\\': \\'string (URL or local path to the model)\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load and preprocess an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Classify the image\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\\\\nprint(\\'Predicted class:\\', predicted_class)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained model for image classification using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 362, "text": " We have several articles to analyze. We need to find a way to compare their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 363, "text": " I have a collection of customer reviews about various products, and I want to find out which reviews are the most similar.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 224, 224, 3])\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Varies depending on the model\\'}, \\'description\\': \\'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 364, "text": " I have some animal photos, I want to train a tool will tell me what animal it is.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 365, "text": " Show me an example where I can cluster similar movie plots.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 366, "text": " Assist me in identifying food entities in grocery store images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Identify and locate objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'input\\': \\'Tensor of shape [batch_size, height, width, 3]\\', \\'output\\': \"Dictionaries with keys \\'detection_boxes\\', \\'detection_classes\\', \\'detection_scores\\'\"}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\\\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP of 0.22\\'}, \\'description\\': \\'A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects in images.\\'}', metadata={})]", "category": "generic"} {"question_id": 367, "text": " Based on the text that my customers give me in different languages, create a vector so I can do customer sentiment analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 368, "text": " I am building a mobile app that can identify different breeds of dogs, and I need fast feature extraction from images to recognize the breeds.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained model for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(image_array)\\\\n\\\\n# Decode the predictions\\\\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)\\\\nprint(class_names)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model based on the MobileNetV2 architecture. It is trained on the ImageNet dataset and can classify images into 1000 different classes. The model is optimized for mobile and edge devices with a small memory footprint and fast inference time.\\'}', metadata={})]", "category": "generic"} {"question_id": 369, "text": " Compare the similarity between two sentences and provide a numerical score.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 370, "text": " I want to analyze customer reviews for sentiment analysis. Provide a method to transform the reviews into fixed-size vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 371, "text": " I need to analyze a group of images and find out which ones are similar. First, I require extracting features of each image for comparison.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False)\\\\n])\\\\n\\\\n# Example input: a batch of images with shape [batch_size, 224, 224, 3].\\\\nimages = tf.random.uniform((4, 224, 224, 3))\\\\n\\\\n# Extract features for the batch of images.\\\\nfeatures = model(images)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset, with 100% depth multiplier and 224x224 input image resolution. Useful for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 372, "text": " In the city, there are many parking lots. We want to count the number of cars parked in a parking lot using a security camera. Can an API help us to do this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 373, "text": " I am going in the forest to observe wildlife, and I want to take a photo of an animal and recognize its species.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 374, "text": " I am trying to build a recommendation system that can match similar customer reviews. Convert the given text into a high-dimensional vector that I can use.\\n###Input: \\\"This product is absolutely amazing! I am extremely happy with my purchase and would highly recommend it to others.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about embeddings.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various tasks\\', \\'accuracy\\': \\'State-of-the-art\\'}, \\'description\\': \\'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 375, "text": " I would like to analyze the sentiments of some sentences by creating an embedding representation of each sentence.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input\\': \\'image\\', \\'output\\': \\'feature_vector\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nfeatures = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API provides a compact representation of an image by extracting feature vectors using MobileNetV2 architecture pretrained on ImageNet dataset. It can be used for tasks like image classification, object detection, and image similarity.\\'}', metadata={})]", "category": "generic"} {"question_id": 376, "text": " Our customer support chat service needs to rank responses by their relevance to solve users' issues. So, we need to get embeddings for a list of response options.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 377, "text": " Let's say my client wants to use an app to identify different dog breeds by uploading a picture. The app should inform them which breed they just uploaded.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 378, "text": " Take a sample sentence and encode it into high-dimensional vector using available API.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 379, "text": " Analyze the news and figure out the topics of the articles.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 380, "text": " I want to build an application that can distinguish different dog breeds from photos. Help me extract features from a set of dog images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 381, "text": " Please create a database of images and their corresponding features using a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 382, "text": " Implement an image classifier to categorize the type of clothing items in a fashion eCommerce dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classifier\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=(224, 224))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\npredictions = model(input_image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained image classifier using TensorFlow Hub to classify images using the MobileNet V2 model.\\'}', metadata={})]", "category": "generic"} {"question_id": 383, "text": " Please suggest an API to quickly and easily classify animals in wildlife pictures using a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection and classification in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 384, "text": " Calculate the feature vector of some images of different fruits.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image feature extraction\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False)\\\\n])\\\\n\\\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\\\nfeatures = model(x)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\\'}', metadata={})]", "category": "generic"} {"question_id": 385, "text": " I need to classify common items found in a household. How do I do this using a pre-trained model?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 386, "text": " I'm organizing an animal-based flashcard game for kids, sort these images by animal content using a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 387, "text": " We need to develop an app to quickly identify plants. Can you provide a pre-trained model to classify plant images?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\'\\\\nmodel = hub.load(model_id)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to load a pre-trained image classification model from TensorFlow Hub using a specific model_id. The model can then be used to classify images into different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 388, "text": " Design a mobile application which requires a custom model to classify 10 different dog breeds based on their photos.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'image_url\\': \\'URL of the image to be classified\\', \\'image_size\\': \\'224x224 pixels\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\ndef classify_image(image_url):\\\\n image = tf.keras.utils.get_file(\\'image.jpg\\', image_url)\\\\n image = tf.keras.preprocessing.image.load_img(image, target_size=(224, 224))\\\\n image = tf.keras.preprocessing.image.img_to_array(image)\\\\n image = tf.expand_dims(image, 0)\\\\n return model(image)\\\\n\\\\nresult = classify_image(\\'https://example.com/image.jpg\\')\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API provides a pre-trained image classification model based on MobileNetV2 architecture, which can classify images into 1000 different categories. The model is trained on ImageNet dataset and can be used for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 389, "text": " We need to identify bird species from some bird images we collected.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 390, "text": " Recommend a neural network layer to obtain a feature vector from a 224x224x3 image for the purpose of similarity search.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False),\\\\n tf.keras.layers.Dense(10, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=\\'adam\\', loss=\\'sparse_categorical_crossentropy\\', metrics=[\\'accuracy\\'])\\\\n\\\\n# Train and evaluate the model\\\\n# model.fit(train_images, train_labels, epochs=5)\\\\n# model.evaluate(test_images, test_labels)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'The MobileNetV2 100 224 feature vector model is a pre-trained neural network that extracts features from images. It is trained on the ImageNet dataset and can be used for various image classification and feature extraction tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 391, "text": " We want to find semantically similar sentences in our customer reviews. Obtain a high-dimensional vector of each review sentence.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 392, "text": " Let's say we have a dataset of images and we want to compress and store them. Design a method for feature extraction and compression.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 393, "text": " I took a picture of a plant in my garden, and I want to identify it using an image classification model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 394, "text": " I'm building a mobile app that classifies images taken by users. The model should be lightweight and fast.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'IMAGE_SHAPE+(3,)\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nIMAGE_SHAPE = (224, 224)\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=IMAGE_SHAPE+(3,))\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=IMAGE_SHAPE)\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nresult = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 395, "text": " I have a collection of images from an automobile trade show and I want to identify the different vehicle models present in those images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the ID of the model to be loaded\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Run the detector on an image\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Varies depending on the model version\\'}, \\'description\\': \\'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 396, "text": " Recommend a solution to classify diseases from the images of plant leaves.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub to classify images using the MobileNet_v2 architecture.\\'}', metadata={})]", "category": "generic"} {"question_id": 397, "text": " We need to extract features from a large set of images to use as input for an image classifier.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 398, "text": " In music recommendation systems, we need to represent the songs as feature vectors. How can we do this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\\\\n\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'This module uses the MobileNet v2 architecture to generate image feature vectors. The model has been pre-trained on the ImageNet dataset and can be used for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 399, "text": " I have a list of user reviews on my shopping website, and I want to cluster them into different categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 400, "text": " Somebody sent me a photo of a car brand's logo, and I need to identify the brand.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 401, "text": " I want to build an image-based recommendation system. Please suggest a way to classify images more efficiently.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 402, "text": " Create a recommendation system that recommends similar products using product descriptions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 403, "text": " Analyze public opinions on a new coffee flavor and find out if they are related to each other.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 404, "text": " I am an app developer and I need a lightweight image classification model to classify images into different categories. How can I do that?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'IMAGE_SHAPE+(3,)\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nIMAGE_SHAPE = (224, 224)\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=IMAGE_SHAPE+(3,))\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=IMAGE_SHAPE)\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nresult = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 405, "text": " I am not good at recognizing sports, sometimes I make mistakes. Therefore, I need a tool to check the answer for me.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about embeddings.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various tasks\\', \\'accuracy\\': \\'State-of-the-art\\'}, \\'description\\': \\'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 406, "text": " Can you please make model to classify type of car\\\\vehicle from an image?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \\'tf.keras.Sequential([hub.KerasLayer(model_id)])\\', \\'api_arguments\\': [\\'model_id: The identifier of the pre-trained model to load\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/1\\'\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\\\\n\\\\n# Load an image and make a prediction\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\npredictions = model.predict(input_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'A pre-trained image classification model using the Inception V3 architecture, available on TensorFlow Hub. The model can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 407, "text": " Analyze online customer reviews and their sentiment by getting an embedding of each review.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'A pre-trained model for embedding text into high-dimensional vectors that can be used for a wide range of tasks such as sentiment analysis, semantic similarity, and classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 408, "text": " I want a system that can find the similarity between different movie reviews.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 409, "text": " My company wants to build a recyclable materials recognition system. We plan to reuse some pre-trained models for transfer learning.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=(299, 299))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\npredictions = model(input_image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'This API is used to load and use pre-trained image classification models from TensorFlow Hub for tasks like object recognition and scene understanding.\\'}', metadata={})]", "category": "generic"} {"question_id": 410, "text": " We want to help our users analyze their posture while performing exercises. Help us detect human poses in images to do that.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image pose detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect human poses in images\\', \\'api_name\\': \\'movenet\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\", \\'api_arguments\\': {\\'input\\': \\'image_tensor\\', \\'output\\': \\'pose_keypoints\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.5.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub\\\\nmovenet = hub.load(\\'https://tfhub.dev/google/movenet/singlepose/lightning/3\\')\\\\npose_keypoints = movenet(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'70.6 mAP\\'}, \\'description\\': \\'MoveNet is a family of pose detection models designed for efficient and accurate detection of human poses in images.\\'}', metadata={})]", "category": "generic"} {"question_id": 411, "text": " We need a way to classify and identify various objects in images, suggest a pre-trained model for that.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\", \\'api_arguments\\': \\'image, score_threshold\\', \\'python_environment_requirements\\': \\'Python 3, TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\\\\nresult = detector(image, score_threshold=0.5)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'mAP@0.5IOU = 32.8%\\'}, \\'description\\': \\'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 412, "text": " Let's take a picture from our hike yesterday, classify the primary plants or flowers in it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[batch_size, 224, 224, 3]\\', \\'output_shape\\': \\'[batch_size, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3), trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture with 100% depth and 224x224 input size. The model is trained on the ImageNet dataset and can be used for feature extraction, fine-tuning, or as a component in a larger model.\\'}', metadata={})]", "category": "generic"} {"question_id": 413, "text": " We are going to develop security cameras to monitor our client's warehouse for unauthorized access.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text using a pre-trained model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text: List of text strings to be embedded\\'], \\'python_environment_requirements\\': [\\'tensorflow>=2.0.0\\', \\'tensorflow-hub>=0.8.0\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 414, "text": " I have a large collection of restaurant reviews in English, and I want to cluster them based on their content.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 415, "text": " Detect the fashion items in given images and create a model for fashion item feature extraction using pretrained image Feature Vector.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 416, "text": " Determine which type of fruit is in the photograph by analyzing and classifying the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 417, "text": " We want to build an image recognition system to classify paintings by artists. Please show us how to use a pretrained model for feature extraction from the paintings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 418, "text": " We want to generate embeddings for a list of movie descriptions so we can later measure the similarity between these movies.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 419, "text": " Tell me what animal I have in my picture. How can I achieve this using a pre-trained TensorFlow model?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 420, "text": " We have a long list of customer reviews for our products. We need to determine which ones are similar so we can improve specific products.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 421, "text": " I have a folder containing images of animals, and I would like to automatically label these images with the name of the animal they contain.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 422, "text": " I have an online marketplace where people sell secondhand items. I would like to categorize the photos of the products people upload.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 423, "text": " Provide me with the ability to identify objects present in a given image, for improving the accessibility of our application.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 424, "text": " I have a collection of paintings and I want to extract their main features to use for indexing among different categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 425, "text": " Calculate the similarity between two paragraphs of text using embeddings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 426, "text": " I'd like to analyze bird songs to identify their species. I need an audio model that can help me extract features from the recordings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Audio feature extraction\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/model_id/1\\')\", \\'api_arguments\\': \\'None\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/model_id/1\\')\\\\nembeddings = model(samples, sample_rate)\", \\'performance\\': {\\'dataset\\': \\'Unknown\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'This API allows you to extract audio features from audio samples using a pre-trained model from TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 427, "text": " Let's create a dog breed classifier and use the pretrained ImageNet MobileNet V2 model to extract dog breed features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 428, "text": " Recommend products similar to the given product description. Use a pre-built model to find the semantic similarity with other product descriptions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 430, "text": " I need to identify the type of food in each picture I have in my album, so I need tensor values of the pretrained model \\\"imagenet-inception_v3-feature_vector\\\".\\n###Input: {\\\"url\\\": \\\"https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'imagenet-mobilenet_v2_130_224-classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\')\", \\'api_arguments\\': {\\'input\\': \\'An image tensor of shape [batch_size, height, width, 3]\\', \\'output\\': \\'A tensor of shape [batch_size, 1001] representing class probabilities\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.9.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\\')\\\\n\\\\n# Preprocess your image to the required format\\\\nimage = tf.keras.preprocessing.image.load_img(\\'your_image.jpg\\', target_size=(224, 224))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(input_image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 83.5%, Top-5 Accuracy: 96.5%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture, trained on ImageNet dataset. The model accepts an image tensor of shape [batch_size, height, width, 3] and outputs class probabilities for each of the 1001 classes.\\'}', metadata={})]", "category": "generic"} {"question_id": 431, "text": " I'm building a chatbot and I need to analyze the similarity between pairs of sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 432, "text": " I captured a photo of an unknown animal, and I want to know the animal's classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 433, "text": " We need a way to detect objects on the road like stop signs, other vehicles, and pedestrians in our autonomous vehicle project. Suggest an API that can help us do that.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 434, "text": " Provide a way to set up an image classification system that automates identification of photographs.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 435, "text": " I am making an AI chatbot that responds to user messages. I need to create sentence embeddings efficiently.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Create image feature vector\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\'\\\\nmodel = hub.load(model_id)\\\\nfeature_vector = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\\'}', metadata={})]", "category": "generic"} {"question_id": 436, "text": " Find a way to showcase the usage of a model to detect objects in a picture taken in a factory to count how many different pieces of equipment are there.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 437, "text": " Convert an image to a feature vector that can be used in content-based image retrieval system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\n\\\\n# Convert the image to a tensor\\\\ninput_tensor = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Run the object detection\\\\nresults = detector(input_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 438, "text": " We're working on a travel blogging platform, and we need a way to extract the features of images that users upload.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.uniform([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Loads a pre-trained image feature vector model from TensorFlow Hub, allowing users to extract features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 439, "text": " Use the embeddings to find cluster seeds for a given set of job descriptions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 440, "text": " The manager wants to build a product recommender system based on the types of items in customers\\u2019 photos. Determine what each item is in the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \\'import tensorflow_hub as hub\\\\nmodel = hub.load(\"https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\")\\\\nsegmentation = model(input_image)\\', \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.897\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\\'}', metadata={})]", "category": "generic"} {"question_id": 441, "text": " Help me find the best matching property images from an image database for a real estate application.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracting feature vectors from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3))\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Extract the feature vector\\\\nfeature_vector = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to extract feature vectors from images using the MobileNetV2 model trained on the ImageNet dataset. The feature vectors can be used for various machine learning tasks, such as image classification, image retrieval, or transfer learning.\\'}', metadata={})]", "category": "generic"} {"question_id": 442, "text": " I have a dataset of photos, and I need to classify all of those photos into different categories, so I'm thinking of using a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 443, "text": " Find objects in city surveillance camera images and get the objects with their corresponding detection scores.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow>=2.0\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\n\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'0.545 mAP\\'}, \\'description\\': \\'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\\'}', metadata={})]", "category": "generic"} {"question_id": 444, "text": " Compare news articles for similarity and rank them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Provides a feature vector for images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': [\\'image_input\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\\\\nfeature_vector = model(image_input)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API provides a feature vector for images using TensorFlow Hub and the Inception V3 model. It can be used for various image-related tasks such as classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 445, "text": " We have some random images and we would like to classify them into different categories based on their context.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 446, "text": " I own a collection of paintings and I need to categorize them based on their depicted object.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 447, "text": " I want to create an app that suggests dog breeds based on the images uploaded by users. Recommend an API for me to use.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 448, "text": " Can you help me classify hotel reviews as positive or negative?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection and classification in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 449, "text": " My kid took a photo of an animal. Please tell me its species.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'This API provides pre-trained image classification models from TensorFlow Hub. Users can load a model using its model_id and make predictions on images.\\'}', metadata={})]", "category": "generic"} {"question_id": 450, "text": " I have images of food items and I need help categorizing them as per the ImageNet categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 451, "text": " Recommend the most relevant workout exercises based on a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 452, "text": " Let's find the semantic similarity between two sentences for our recommendation system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 453, "text": " I need to separate different objects in an image, like differentiate the road, house and tree.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 454, "text": " We want to analyze paintings based on their visual characteristics and need a way to represent them numerically.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 455, "text": " Our company has a lot of images that we need to categorize into one of 1000 classes. We're looking for a pretrained model that could help.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\ninput_image = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(input_image)\\\\ninput_batch = tf.expand_dims(input_array, 0)\\\\npredictions = model(input_batch)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using TensorFlow Hub that can classify images into one of 1000 classes. The example code demonstrates how to load the model, preprocess an input image, and obtain predictions.\\'}', metadata={})]", "category": "generic"} {"question_id": 457, "text": " I have a collection of images and I need to categorize them. Please generate predictions using a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 458, "text": " A user wants to know how many objects are in an image they have taken with their phone. Calculate the count by processing the image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nloaded_model = hub.load(model_id)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load an image classification model from TensorFlow Hub by providing the model_id. This example demonstrates how to load the Inception V3 model.\\'}', metadata={})]", "category": "generic"} {"question_id": 459, "text": " Create a program that alerts the user when a dog is at the door, by identifying the dogs in the pictures from a live webcam feed.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 460, "text": " I want to analyze a group of sentences, find if there are similar ones so that I can group them together. Help me get embeddings for these sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 461, "text": " Are there any good pretrained image classifiers? I want to try one on this image and get top 5 predictions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio event classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Classify audio events\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/yamnet/1\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load audio file and get waveform\\\\naudio_file = \\'path/to/audio/file.wav\\'\\\\nwaveform, sample_rate = tf.audio.decode_wav(tf.io.read_file(audio_file), desired_channels=1)\\\\n\\\\n# Run the model on the waveform\\\\nclass_scores, embeddings, spectrogram = model(waveform)\\\\n\\\\n# Get the top class\\\\ntop_class = tf.argmax(class_scores, axis=-1)\\\\n\\\\n# Print the top class\\\\nprint(\\'Top class:\\', top_class.numpy())\", \\'performance\\': {\\'dataset\\': \\'AudioSet\\', \\'accuracy\\': \\'0.745 mAP\\'}, \\'description\\': \"YAMNet is a pretrained deep net that predicts 521 audio event classes based on the AudioSet dataset. It\\'s useful for audio event classification tasks.\"}', metadata={})]", "category": "generic"} {"question_id": 462, "text": " We are creating an app to recognize different dog breeds. I need to find a pre-trained model capable of helping me with this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 463, "text": " I am starting an e-commerce platform, and I want to build a feature vector database of different products to identify and categorize them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 464, "text": " I have pictures of animals and I want to identify them by their species.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 465, "text": " We're creating a database of different foods, and I want to generate feature vectors for each food item from their images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate feature vectors for images\\', \\'api_name\\': \\'imagenet_mobilenet_v3_large_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5\\', input_shape=(224, 224, 3), output_shape=(1280,))\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Generate feature vector\\\\nfeature_vector = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'75.3%\\'}, \\'description\\': \\'This API provides a pre-trained image feature vector model, MobileNetV3-Large, from TensorFlow Hub. The model is trained on the ImageNet dataset and can be used to generate feature vectors for images of size 224x224 with 3 channels.\\'}', metadata={})]", "category": "generic"} {"question_id": 466, "text": " I need to build a customer support chatbot that automatically replies to user queries. How can I represent sentences as numerical vectors for semantic similarity comparison?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text embedding using a pre-trained model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello, world!\\', \\'Embed text with Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 467, "text": " In order to build a dogs versus cats classifier, I need a good feature extractor for images. Can you provide me with one?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=[224, 224, 3],\\\\n output_shape=[1280],\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=\\'adam\\', loss=\\'categorical_crossentropy\\', metrics=[\\'accuracy\\'])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API provides a pre-trained MobileNetV2 model for extracting feature vectors from images. The model is trained on the ImageNet dataset and can be used as a feature extractor for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 468, "text": " Analyze an image we have taken and categorize the primary subject of that image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 469, "text": " For analyzing customer reviews, I want to convert them into high-dimensional embeddings that we will use for sentiment analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS-Benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors for use in natural language processing tasks such as sentiment analysis, translation, and text classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 470, "text": " Please determine which two sentences from a group are semantically similar based on their embeddings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 471, "text": " Given a dataset of images, we need to extract their feature vectors for use in an image search engine application.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\", \\'api_arguments\\': \\'image, score_threshold\\', \\'python_environment_requirements\\': \\'Python 3, TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\ndetector = hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\\')\\\\nresult = detector(image, score_threshold=0.5)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'mAP@0.5IOU = 32.8%\\'}, \\'description\\': \\'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 472, "text": " I want an AI to classify what's inside a photo by loading a pre-trained model from TensorFlow Hub.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 473, "text": " Preserve this art piece in a digital archive. Extract its features for future analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 474, "text": " How can we extract image features to compare the similarity between two interior designs?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 475, "text": " We have a survey containing phrases on the job market. We want to get a visualization of the job market. Can you provide an embedding of the survey phrases?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 476, "text": " Try to identify the object in the image using a pre-trained model built on the TensorFlow Hub platform.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\n\\\\n# Convert the image to a tensor\\\\ninput_tensor = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Run the object detection\\\\nresults = detector(input_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 477, "text": " In a warehouse, we would like to use a model to identify items stocked on the shelves by detecting objects in images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use an object detection model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'The id of the model to be loaded from TensorFlow Hub\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\ndetected_objects = model(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Unknown\\'}, \\'description\\': \\'Load and use an object detection model from TensorFlow Hub for detecting objects in images.\\'}', metadata={})]", "category": "generic"} {"question_id": 478, "text": " I have a collection of butterfly images, so please find the image feature vectors for me.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 479, "text": " I need to create a program that finds how similar two given sentences are. To do this, I first want to encode the sentences into high-dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': {\\'model_id\\': \\'string\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into various categories. The example provided demonstrates how to load the Inception V3 model and make predictions on a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 480, "text": " We need to find similar phrases within a set of sentences. To do this, first, we will encode these sentences into vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data for machine learning models\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'List of sentences or phrases\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world\\', \\'Embedding sentences with TensorFlow Hub\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 481, "text": " Translate the following English text into a semantic embedding: \\\"The quick brown fox jumps over the lazy dog.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS Benchmark\\', \\'accuracy\\': \\'0.803\\'}, \\'description\\': \\'A model for encoding variable-length English text into fixed-length high-dimensional vectors. It is useful for applications such as semantic similarity, clustering, or text classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 482, "text": " Compare the similarity of the following two sentences: \\\"The quick brown fox jumps over the lazy dog,\\\" and \\\"A speedy reddish-orange mammal leaps over a resting canine.\\\"\\n###Input: \\\"The quick brown fox jumps over the lazy dog\\\", \\\"A speedy reddish-orange mammal leaps over a resting canine.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 483, "text": " We would like to use transfer learning for an image classification problem. Extract features from images using a pre-trained feature vector model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 484, "text": " Calculate the similarity between two sentences for a recommendation system based on the content.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 485, "text": " Calculate the semantic similarities between the product descriptions of two items I found on an e-commerce site.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 486, "text": " As we need to find the similarity among the users' reviews, we should preprocess the raw text to remove unwanted characters and get the corresponding text embedding.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 487, "text": " A real-estate company wants to use computer vision to classify images of homes into multiple categories like 'modern', 'rustic', etc. Provide a suitable API to extract image feature vectors for building the classifier.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract feature vectors from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n trainable=False)\\\\n])\\\\n\\\\nimage = tf.keras.Input(shape=(224, 224, 3))\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A pre-trained image feature vector model that uses MobileNetV2 architecture to extract feature vectors from images. It is trained on the ImageNet dataset and can be used for various computer vision tasks, such as image classification, object detection, and image similarity.\\'}', metadata={})]", "category": "generic"} {"question_id": 488, "text": " I have a list of customer reviews and want to group them based on their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 489, "text": " Analyze customer feedback and group them into positive and negative sentiments.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 490, "text": " To classify silhouettes, extract image features with a pre-trained model then build a new classification model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\'\\\\nmodel = hub.load(model_id)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to load a pre-trained image classification model from TensorFlow Hub using a specific model_id. The model can then be used to classify images into different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 491, "text": " Measure the similarity between two strings, we want it encoded representations.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 492, "text": " I am building a news article clustering application. I need to get the embeddings of each news article to compare their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 493, "text": " Help me find similar images in a large database for a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 494, "text": " I want to recognize who is who in my photos, so create a model to turn the photos into vectors first.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 495, "text": " Analyze the pictures animals have taken and sort them into different animal categories. To do this, derive the feature vectors from their images so that I can train a classifier.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 496, "text": " I am creating a question-answering system and need to find the closest matching question in my database to a new user's question. Please provide the embeddings for the questions.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': {\\'input\\': \\'List of sentences\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world\\', \\'I am learning how to use the Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs.\\'}', metadata={})]", "category": "generic"} {"question_id": 497, "text": " I took some pictures during my vacation and now I want to create a collection of images and organize them based on image features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 498, "text": " I need to classify plant species using a machine learning model to know which type they are. Can I use this model to classify images of plants?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 499, "text": " We want to cluster similar product reviews to understand the customer experience better.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 500, "text": " Write a code for embedding the given sentences into high-dimensional vectors to find similarity in their meanings.\\n###Input: [\\\"I have a cat.\\\", \\\"My pet is a feline.\\\"]\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text embedding using a pre-trained model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello, world!\\', \\'Embed text with Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 501, "text": " Assist me in creating a model for image classification by extracting features from images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 502, "text": " We are facing an issue while categorizing different plants species in our garden. Could you please assist and identify them for us?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 503, "text": " I want to create a custom image classifier for a project but don't want to train a model from scratch. What technology can I leverage to achieve this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 504, "text": " We're organizing a car show, and we want to classify the different car models automatically using an application.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 505, "text": " I want to create an AI-driven customer service chatbot. How can I create a similarity measure for the customer queries and the possible answers?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Create image feature vector\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\'\\\\nmodel = hub.load(model_id)\\\\nfeature_vector = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\\'}', metadata={})]", "category": "generic"} {"question_id": 506, "text": " For understanding the contents of user reviews, I would like to transform comments into numerical representations to be utilized in a machine learning model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 507, "text": " I need to create chatbot responses for similar questions. Convert these sentences into vectors that can be clustered.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Create image feature vector\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\'\\\\nmodel = hub.load(model_id)\\\\nfeature_vector = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\\'}', metadata={})]", "category": "generic"} {"question_id": 508, "text": " I want to compare a large image dataset of different car models to suggest visually related recommendations to our users.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 509, "text": " I am working on a project and I need to find similar images in my dataset. Extract feature vectors of images for comparison.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracting feature vectors from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3))\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Extract the feature vector\\\\nfeature_vector = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to extract feature vectors from images using the MobileNetV2 model trained on the ImageNet dataset. The feature vectors can be used for various machine learning tasks, such as image classification, image retrieval, or transfer learning.\\'}', metadata={})]", "category": "generic"} {"question_id": 510, "text": " We need to generate embeddings for product descriptions to cluster similar products together. Get an embedding for each product description.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 511, "text": " Capture the most populated areas of a city from aerial images taken from drones.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 512, "text": " Determine what objects are visible in a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 513, "text": " We need to build a content-based image search feature on our website.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using a pre-trained image feature vector model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(1)])\\\\n\\\\nmodel.build([None, 224, 224, 3])\\\\nmodel.summary()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\\'}', metadata={})]", "category": "generic"} {"question_id": 514, "text": " Find out what's in the picture and recommend related products to customers.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 515, "text": " Calculate the similarity between two sentences using their embeddings. \\n###Input: \\\"The cat is a domestic animal.\\\", \\\"The cat is a household pet.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 517, "text": " How can we transform sentences into semantic vectors for a similarity search engine?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text embedding using a pre-trained model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello, world!\\', \\'Embed text with Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 518, "text": " Generate a detailed map caption of images captured by a drone over a natural disaster-affected area.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \\'import tensorflow_hub as hub\\\\nmodel = hub.load(\"https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\")\\\\nsegmentation = model(input_image)\\', \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.897\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\\'}', metadata={})]", "category": "generic"} {"question_id": 519, "text": " I took a photo of my dog at the park, and I want to know its breed.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 520, "text": " Find out what kind of trees are in our park using a pre-trained deep learning model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'output_shape\\': \\'[1001]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'2.0\\', \\'tensorflow_hub\\': \\'0.7\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', output_shape=[1001])\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\\\nimage = tf.expand_dims(image, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(image)\\\\n\\\\n# Decode predictions\\\\ndecoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=5)\\\\n\\\\nfor i, (imagenet_id, label, score) in enumerate(decoded_predictions[0]):\\\\n print(\\'{}. {}: {:.2f}%\\'.format(i + 1, label, score * 100))\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'MobileNetV2 is a lightweight deep learning model for image classification. It is pre-trained on the ImageNet dataset and can be loaded using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 521, "text": " In order to analyze art pieces, extract and save feature vectors of famous paintings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extracts feature vectors from images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nfeatures = model.predict(image)\\\\nprint(features)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This API provides a pre-trained Inception V3 model for extracting feature vectors from images. It is built on TensorFlow Hub and requires TensorFlow and TensorFlow Hub packages. The example code demonstrates how to load an image, preprocess it, and extract features using the API.\\'}', metadata={})]", "category": "generic"} {"question_id": 522, "text": " I would like to create a mobile app to quickly identify the type of tree based on a photo of its leaves.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 523, "text": " A farmer wants to analyze the images of soil to classify the type and quality of soil.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 524, "text": " Identify the contents of an image by loading a pretrained model and classify it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id: The ID of the pre-trained model to be used for image classification\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\\\n\\\\n# Make a prediction\\\\npredictions = model(tf.expand_dims(image, 0))\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows users to perform image classification using a pre-trained TensorFlow Hub model. The model can be loaded using the model_id, and the API provides functionality for loading and preprocessing images, as well as making predictions.\\'}', metadata={})]", "category": "generic"} {"question_id": 525, "text": " While traveling, I want to use the API to detect various landmarks and monuments by snapping photos of them with my smartphone.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path_to_image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows users to load and use pre-trained image classification models from TensorFlow Hub. The models can be used to classify images into various categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 526, "text": " Design a system for customer reviews classification based on the level of satisfaction.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image feature vector\\', \\'api_name\\': \\'imagenet_mobilenet_v3_small_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\\',\\\\n input_shape=(224, 224, 3))\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Extract the feature vector\\\\nfeature_vector = model.predict(image_array)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'65.4%\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV3 small architecture for TensorFlow Hub, which can be used for transfer learning or as a feature extractor for image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 527, "text": " Provide me with a way to measure semantic similarity between two input sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 528, "text": " In the pet store, we discovered an unknown animal. Help us identify the animal.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v3_small_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[batch_size, 224, 224, 3]\\', \\'output_shape\\': \\'[batch_size, 1024]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\\', input_shape=(224, 224, 3), trainable=False),\\\\n tf.keras.layers.Dense(10, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=\\'adam\\', loss=\\'categorical_crossentropy\\', metrics=[\\'accuracy\\'])\\\\n\\\\n# Train the model with your data\\\\n# model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'61.9%\\'}, \\'description\\': \\'This module is an image feature vector module that takes an input image of size 224x224x3 and outputs a feature vector of size 1024. It uses the MobileNetV3-small model architecture with a width multiplier of 1.0, pretrained on the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 529, "text": " We have a set of movie reviews and need to generate embeddings for each review to perform sentiment analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 530, "text": " Help me load a pre-trained image classification model to categorize fruits for my new dietary app project.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 531, "text": " I have a collection of images from a fashion show, and I want to extract the features of each image to use in a recommendation engine.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.random.normal([1, 299, 299, 3])\\\\nfeatures = model(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\\'}', metadata={})]", "category": "generic"} {"question_id": 532, "text": " I am working on analyzing customer reviews of a product, and I want to represent the text data in a way that is easy to work with. What is an API to efficiently convert textual data into numerical representations?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 533, "text": " We are developing an article recommendation system. Preprocess the following article title: \\\"Latest Advances in Artificial Intelligence and Machine Learning\\\".\\n###Input: Latest Advances in Artificial Intelligence and Machine Learning\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'ssd_mobilenet_v2\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\\')\", \\'api_arguments\\': {\\'image\\': \\'Input image tensor\\', \\'num_detections\\': \\'Number of detected objects\\', \\'detection_boxes\\': \\'Bounding boxes for detected objects\\', \\'detection_classes\\': \\'Class labels for detected objects\\', \\'detection_scores\\': \\'Confidence scores for detected objects\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ndetector = hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\\')\\\\n\\\\n# Preprocess image\\\\ninput_image = tf.image.resize(image, (640, 640))\\\\ninput_image = tf.expand_dims(input_image, axis=0)\\\\n\\\\n# Run detector\\\\noutput = detector(input_image)\\\\n\\\\n# Extract results\\\\nnum_detections = output[\\'num_detections\\'].numpy()\\\\ndetection_boxes = output[\\'detection_boxes\\'].numpy()\\\\ndetection_classes = output[\\'detection_classes\\'].numpy()\\\\ndetection_scores = output[\\'detection_scores\\'].numpy()\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP 0.32\\'}, \\'description\\': \\'A pre-trained object detection model based on the SSD Mobilenet V2 architecture, capable of detecting multiple objects in an image. The model is trained on the COCO dataset and achieves a mean Average Precision (mAP) of 0.32.\\'}', metadata={})]", "category": "generic"} {"question_id": 534, "text": " Imagine you are building a recommendation system that suggests articles to users on a website. To accomplish that, you need an embedding for each article title in order to compare their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 535, "text": " Tell me what kind of flower is in this image by classifying it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 536, "text": " Verify if the following two sentences are related by measuring the semantic similarity: Sentence 1: \\\"The sky is blue today.\\\" Sentence 2: \\\"Today the atmosphere appears azure.\\\"\\n###Input: The sky is blue today. Today the atmosphere appears azure.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_130_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'MobileNetV2 is a family of neural network architectures for efficient on-device image classification and related tasks, originally published by Mark Sandler, et al. This model is pre-trained on the ImageNet dataset and can be used for various image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 537, "text": " Can you help me find a location of certain objects after providing some images?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 538, "text": " We need to get image feature vectors and build a simple image classifier for our image dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 539, "text": " My friend sent me a picture of his new dog breed, but I can't recognize it. Can you classify this image for me?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 540, "text": " We have to recognize a variety of animals and classify them in a zoo, using a lightweight model that can work on mobile phones.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'IMAGE_SHAPE+(3,)\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nIMAGE_SHAPE = (224, 224)\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=IMAGE_SHAPE+(3,))\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=IMAGE_SHAPE)\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nresult = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 541, "text": " I've got an article in English, and I'd like to analyze it by clustering similar paragraphs together. How would I go about turning each paragraph of text into a high-dimensional vector?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text preprocessing\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'text_input\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'0.78\\'}, \\'description\\': \\'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\\'}', metadata={})]", "category": "generic"} {"question_id": 542, "text": " Tell me how to build a model using feature extraction from images, for use in a binary classification task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3), output_shape=(1280,))\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for extracting feature vectors from images, using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 543, "text": " I work for an art gallery looking for a solution to classify images of our collection by art style.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the ID of the model to be loaded\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Run the detector on an image\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Varies depending on the model version\\'}, \\'description\\': \\'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 544, "text": " I just bought a new drone and I want to capture a street video where I can recognize the types of vehicles approaching.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'input\\': \\'Image tensor\\', \\'output\\': \\'Segmentation mask tensor\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\\\\nimage_tensor = tf.image.resize(image, (257, 257))\\\\nsegmentation_mask = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.899\\'}, \\'description\\': \\'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 545, "text": " I want to measure the similarity between two images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 546, "text": " Provide me with a tool to create an image classifier by extracting features from images in a dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 547, "text": " Figure out a way to extract valuable features from images of potential clients' products to compare their similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 548, "text": " My personal trainer wants me to analyze my workout form. Can you help me detect my body position in a photo?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in an image\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'image.jpg\\'))\\\\nresult = detector([image])\", \\'performance\\': {\\'dataset\\': \\'OpenImages_v4\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\\'}', metadata={})]", "category": "generic"} {"question_id": 549, "text": " Can you help me highlight animals in my vacation photographs?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract image features\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'False\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.5.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False), # Can be True, see below.\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 550, "text": " Detect objects and their locations on this image I just took of my room.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow>=2.0\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\n\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'0.545 mAP\\'}, \\'description\\': \\'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\\'}', metadata={})]", "category": "generic"} {"question_id": 551, "text": " Create a flower classification model using feature vectors pre-extracted from images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 552, "text": " I am running a wildlife conservation project, and I need help to identify different species of animals and their locations in the images taken by camera traps.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow>=2.0\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\n\\\\nresult = detector(image_tensor)\\\\nprint(result)\", \\'performance\\': {\\'dataset\\': \\'Open Images v4\\', \\'accuracy\\': \\'0.545 mAP\\'}, \\'description\\': \\'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\\'}', metadata={})]", "category": "generic"} {"question_id": 553, "text": " We need to detect spam emails based on their subjects. Retrieve a high-dimensional vector from each email subject to be used for classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nloaded_model = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage_np = np.array(image)\\\\n\\\\ninput_tensor = tf.convert_to_tensor(image_np)\\\\ninput_tensor = input_tensor[tf.newaxis, ...]\\\\n\\\\noutput_dict = loaded_model(input_tensor)\\\\n\\\\nboxes = output_dict[\\'detection_boxes\\'][0].numpy()\\\\nscores = output_dict[\\'detection_scores\\'][0].numpy()\\\\nclasses = output_dict[\\'detection_classes\\'][0].numpy().astype(np.int32)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 554, "text": " There are a bunch of user reviews about our new product, and we need assistance categorizing them into different sentiments.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input: List of text strings\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about text embedding\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 555, "text": " We have images that we want to analyze, and I hope I can find a way to extract features from these images for further analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 556, "text": " What is a good way to find the similarity between two sentences?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 557, "text": " I'm a clothing store owner, and I want to create a recommendation system that suggests similar clothes by comparing their features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 558, "text": " I want to build a plant identifier app, and I need to determine the type of plant in a picture taken from the app.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 559, "text": " Can you please identify whether these two sentenes share a similar meaning or not?\\n###Input: \\\"The quick brown fox jumps over the lazy dog.\\\", \\\"A speedy, sly fox leaps over a lounging canine.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 560, "text": " I want to create a custom image classifier, but first I need to use a pre-trained model to extract features from my dataset images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 561, "text": " Recommend a mobile app for automatically classifying images taken by the user.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'mobilenet_v2\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', \\'input_shape\\': \\'IMAGE_SHAPE+(3,)\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nIMAGE_SHAPE = (224, 224)\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\', input_shape=IMAGE_SHAPE+(3,))\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=IMAGE_SHAPE)\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\nresult = model.predict(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 562, "text": " We want to find semantic similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 563, "text": " We want to develop a smart surveillance system to monitor intruders. Please help us identify humans present in the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 564, "text": " My friends uploaded a collection of photos from our trip, and I want to organize them by identifying what is in each photo.\\n###Input: \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \\'import tensorflow_hub as hub\\\\nmodel = hub.load(\"https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\")\\\\nsegmentation = model(input_image)\\', \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.897\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\\'}', metadata={})]", "category": "generic"} {"question_id": 565, "text": " Identify which model is used to encode text for natural language processing tasks like text classification and semantic similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional space\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'Semantic Textual Similarity\\', \\'accuracy\\': \\'0.8\\'}, \\'description\\': \\'This API provides a pre-trained model to convert text into high-dimensional space, which can be used for various natural language processing tasks such as semantic similarity, clustering, and classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 566, "text": " Calculate the similarity between the following two sentences: \\\"The quick brown fox jumps over the lazy dog\\\" and \\\"The fast auburn fox leapt over the idle canine.\\\"\\n###Input: \\\"The quick brown fox jumps over the lazy dog\\\", \\\"The fast auburn fox leapt over the idle canine.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 567, "text": " Construct a recommendation engine for a forum that finds similar threads based on the text content.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 568, "text": " They have a dataset of stained glass images and want to organize them into categories or classes. They need a pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and run a pre-trained image classification model\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Run the model on a sample image\\\\nsample_image = tf.zeros([1, 224, 224, 3])\\\\nlogits = model(sample_image)\\\\npredicted_class = tf.argmax(logits, axis=-1)\\\\nprint(predicted_class)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows you to load and run a pre-trained MobileNetV2 image classification model from TensorFlow Hub. The model is trained on the ImageNet dataset and can classify images into 1000 different categories with an accuracy of 71.9%.\\'}', metadata={})]", "category": "generic"} {"question_id": 569, "text": " Provide me with a method to understand the semantic similarity between movie reviews.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 570, "text": " We have a large photo archive, and we need a model to recognize and categorize pictures based on their content.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 571, "text": " After a dinner party, I want to save the pictures taken during the party and find similar images for decoration purposes by extracting an image feature vector.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 572, "text": " I am trying to create clusters of similar customer feedback for a product.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 573, "text": " I have pictures of animals, and I need to find out what type of animals they are by leveraging pre-trained models.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 574, "text": " We are working on an app that can identify plants. We have the images of the leaves, so all we need is a pre-trained model to classify the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 575, "text": " Analyze the sentiments of these customer reviews before processing their feedback.\\n###Input: [\\\"I loved the service and the food!\\\", \\\"The staff was rude, and I had a terrible experience.\\\", \\\"Not too bad, could be better.\\\"]\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nresult = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Varies depending on the model\\'}, \\'description\\': \\'TensorFlow Hub provides a variety of pre-trained image classification models that can be easily loaded and used for various image classification tasks. The models are trained on the ImageNet dataset and their performance varies depending on the specific model chosen.\\'}', metadata={})]", "category": "generic"} {"question_id": 576, "text": " We want to obtain the location of a boat in an image. Detect objects in the given image and provide the location of the boat if found.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 577, "text": " Create a system to find the similarity between two sentences.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 578, "text": " I'm building a recommendation system for similar pictures. Provide me with a vector representation of a picture to measure image similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[None, 224, 224, 3]\\', \\'output_shape\\': \\'[None, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3])\\\\n])\\\\n\\\\nfeatures = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A compact representation of the MobileNet V2 model trained on ImageNet for image feature extraction. It accepts an input of shape [None, 224, 224, 3] and outputs a feature vector of shape [None, 1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 579, "text": " Recommend to me some suitable products based on the textual similarity of their descriptions from a database.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 580, "text": " Extract audio features to classify sounds in the environment.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Audio feature extraction\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': [{\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/yamnet/1\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load a wav file\\\\nfilename = \\'audio_file.wav\\'\\\\nfile_contents = tf.io.read_file(filename)\\\\nwaveform = tf.audio.decode_wav(file_contents).audio\\\\n\\\\n# Extract features\\\\nscores, embeddings, spectrogram = model(waveform)\", \\'performance\\': {\\'dataset\\': \\'AudioSet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \"YAMNet is a deep net that predicts 521 audio event classes based on the AudioSet dataset. It\\'s used for audio feature extraction and can be used for tasks like audio classification, tagging, and recommendation.\"}', metadata={})]", "category": "generic"} {"question_id": 581, "text": " Write an AI-powered program to summarize movie plots while retaining maximum details from the given plot.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': {\\'model_id\\': \\'string\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into various categories. The example provided demonstrates how to load the Inception V3 model and make predictions on a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 582, "text": " Help me compare similarities between user reviews about two different products.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 583, "text": " Provided with images of paintings, I want to determine which art era they belong to.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 584, "text": " Understand the relationship between two movie synopses and determine whether they seem similar or not.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 585, "text": " Suggest personalized ads to users by creating a semantic similarity model that recommends advertisements based on textual similarity to user preferences stored in the description field. \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 586, "text": " There is a list of customer reviews and your task is to calculate the semantic similarity between each pair of reviews.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 587, "text": " Expose a method to compute the semantic similarity of pairs of sentences by comparing the high-dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 588, "text": " I am analyzing tweets, and I want to know the sentiment of each tweet. Please help me convert tweets into meaningful representations.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 589, "text": " I need to build a machine learning model that can automatically detect and classify different food items from images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 590, "text": " Identify the animal in the photo I have taken.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 591, "text": " Generate an appropriate label for instance of image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the ID of the model to be loaded\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Run the detector on an image\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Varies depending on the model version\\'}, \\'description\\': \\'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 592, "text": " I'm traveling to a new city and I want to use my phone to identify different objects in my surroundings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 593, "text": " Analyze a list of movie reviews and group them based on semantic similarity by first converting the reviews into embeddings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text preprocessing\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'text_input\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'0.78\\'}, \\'description\\': \\'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\\'}', metadata={})]", "category": "generic"} {"question_id": 594, "text": " I am working on a car accident detection system. Show me how to detect object and classify them in images\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection and classification in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\\'}', metadata={})]", "category": "generic"} {"question_id": 595, "text": " Identify the objects present in the picture I took from my last hiking trip.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the TensorFlow Hub model URL\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Detect objects in the image\\\\nresult = model(image)\\\\n\\\\n# Print detected objects\\\\nfor obj in result[\\'detection_boxes\\']:\\\\n print(obj)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\\'}', metadata={})]", "category": "generic"} {"question_id": 596, "text": " We are creating a visual recommender system based on images. We need to extract features from them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[batch_size, 224, 224, 3]\\', \\'output_shape\\': \\'[batch_size, 1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224,224,3),\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model that can be used to extract features from images. The model is based on the MobileNetV2 architecture and is trained on the ImageNet dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 597, "text": " In order to analyze the sentiment of tweets on Twitter, I need the Universal Sentence Encoder model to convert tweets into embeddings for analysis.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into a high-dimensional vector\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'input_text\\', \\'python_environment_requirements\\': \\'tensorflow, tensorflow_hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.803521\\'}, \\'description\\': \\'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 598, "text": " Detect and count the number of different objects in a picture I took at a beach.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the TensorFlow Hub model URL\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Detect objects in the image\\\\nresult = model(image)\\\\n\\\\n# Print detected objects\\\\nfor obj in result[\\'detection_boxes\\']:\\\\n print(obj)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\\'}', metadata={})]", "category": "generic"} {"question_id": 599, "text": " I am visiting different places and I want to know which landmark I am visiting.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about embeddings.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various tasks\\', \\'accuracy\\': \\'State-of-the-art\\'}, \\'description\\': \\'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 600, "text": " Identify different types of birds when hikers send the images to our system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 601, "text": " How can I compare the similarity between multiple sentences?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 602, "text": " Tell me what kind of animal/bird is in this photo I just took.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\nresult = detector(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 603, "text": " Determine the top 3 closest category matches for a given image using the Inception V3 pre-trained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': {\\'model_id\\': \\'string\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, axis=0)\\\\n\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into various categories. The example provided demonstrates how to load the Inception V3 model and make predictions on a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 604, "text": " I need a simple way to encode text into vectors for my clustering algorithm.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 605, "text": " Find a tool to extract features from images and create a model to identify different types of plants.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 606, "text": " How do I create image-feature-vector for the last travel destination photo album to organize similar pictures together?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 607, "text": " Identify the make and model of a car using an image posted on an online car marketplace.\\n###Input: \\\"https://url-to-image-of-car-from-marketplace\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \\'tf.keras.Sequential([hub.KerasLayer(model_id)])\\', \\'api_arguments\\': [\\'model_id: The identifier of the pre-trained model to load\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/1\\'\\\\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\\\\n\\\\n# Load an image and make a prediction\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_array = tf.expand_dims(input_array, 0)\\\\npredictions = model.predict(input_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'A pre-trained image classification model using the Inception V3 architecture, available on TensorFlow Hub. The model can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 608, "text": " Assist me in identifying animal species present in a collection of images for an ecological study.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the ID of the model to be loaded\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Run the detector on an image\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Varies depending on the model version\\'}, \\'description\\': \\'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 609, "text": " I am building a tool to measure the similarity between different product descriptions. To do this, I need to first get embeddings for each description.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 610, "text": " Show me how to classify an image of a car into one of the 1000 categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\ninput_image = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\ninput_array = tf.keras.preprocessing.image.img_to_array(input_image)\\\\ninput_batch = tf.expand_dims(input_array, 0)\\\\npredictions = model(input_batch)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using TensorFlow Hub that can classify images into one of 1000 classes. The example code demonstrates how to load the model, preprocess an input image, and obtain predictions.\\'}', metadata={})]", "category": "generic"} {"question_id": 611, "text": " We need to create an image classifier for recognizing dog breeds. Before creating the classifier, we should extract a fixed-length feature vector from each image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Create image feature vector\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\'\\\\nmodel = hub.load(model_id)\\\\nfeature_vector = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\\'}', metadata={})]", "category": "generic"} {"question_id": 612, "text": " Help me create a machine learning model to identify whether a solar panel is damaged or not using images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 613, "text": " Suppose you want to build a search engine that can find the most visually similar products in your online shop. You need to extract feature vectors from product images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 614, "text": " I am a teacher and want to classify images related to educational material. Find out the category of educational images by providing the image's URL.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nloaded_model = hub.load(model_id)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load an image classification model from TensorFlow Hub by providing the model_id. This example demonstrates how to load the Inception V3 model.\\'}', metadata={})]", "category": "generic"} {"question_id": 615, "text": " Can you find the semantic similarity between a list of sentences given their embeddings obtained from a pre-trained model?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text embedding using a pre-trained model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello, world!\\', \\'Embed text with Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 616, "text": " An airport authority wants to detect the presence of drones close to the airport runway.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 617, "text": " I have a lot of images and I want to categorize them based on the objects present in the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nimage = tf.image.decode_jpeg(tf.io.read_file(\\'path/to/image.jpg\\'))\\\\nresult = detector(image[tf.newaxis, ...])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 618, "text": " I'm planning to do sentiment analysis on a bunch of news articles. Help me convert the article content to 20-dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio Speech-to-Text\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Convert audio speech to text\\', \\'api_name\\': \\'wav2vec2_large_xlsr_53\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\\')\", \\'api_arguments\\': {\\'audio_input\\': \\'wav file\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.5.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub\\\\nmodel = hub.load(\\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\\')\\\\ntranscript = model.signatures[\\'serving_default\\'](audio_input)[\\'output_0\\']\", \\'performance\\': {\\'dataset\\': \\'CommonVoice\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained model to convert audio speech to text using wav2vec2_large_xlsr_53 architecture from TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 619, "text": " In order to perform image classification using a machine learning model, I need to generate feature vectors for the given images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract feature vectors from images\\', \\'api_name\\': \\'inception_v3\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': {\\'input\\': \\'image_tensor\\', \\'output\\': \\'feature_vector\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nimage_tensor = tf.zeros([1, 299, 299, 3])\\\\nfeature_vector = hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')(image_tensor)\\\\nprint(feature_vector.shape)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows you to extract feature vectors from images using the Inception V3 model, which is pre-trained on the ImageNet dataset. The feature vectors can be used for various machine learning tasks, such as image classification, object detection, and image similarity.\\'}', metadata={})]", "category": "generic"} {"question_id": 620, "text": " Generate embeddings for movie descriptions to create a recommendation system based on semantic similarity.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 621, "text": " Match products descriptions to our customer search query in our e-commerce store. First, encode them into vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 622, "text": " For a fashion e-commerce website, I want to build a recommendation system on clothing items based on their similarity. How do I extract feature vectors from clothing images for comparison?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 623, "text": " For my language model, I require preprocessed text. Use an appropriate method to assist me in preparing the input text for further processing.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 624, "text": " We are trying to cluster movie reviews from the Internet. First, we want to transform the reviews into fixed dimension vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 625, "text": " Calculate semantic similarity between input sentences using a pre-trained sentence embedding model.\\n###Input: \\\"The cat is on the roof.\\\", \\\"A feline is atop the building.\\\", \\\"We're attending a business conference.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text embedding using a pre-trained model\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub; embed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\'); embeddings = embed([\\'Hello, world!\\', \\'Embed text with Universal Sentence Encoder\\'])\", \\'performance\\': {\\'dataset\\': \\'various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 626, "text": " I want to recommend restaurants based on their reviews. Cluster similar reviews together.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 627, "text": " Help me to identify different handbags from pictures by extracting features.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280])\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\\'}', metadata={})]", "category": "generic"} {"question_id": 628, "text": " Can you help me create a new image classification model by using a pre-trained model to extract image features and then build my custom classifier on top?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 629, "text": " I want to analyze product reviews and create a classifier to automatically sort them based on their positive or negative sentiment.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 630, "text": " I need a way to identify celebrities in pictures I take on my phone. Can you use image classification to do this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 631, "text": " Identify the primary sound category in an audio clip.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 632, "text": " Create a model for bird species identification that extracts features considering memory constraints when deploying on an IoT device.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3])\\\\n])\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.expand_dims(image, 0)\\\\n\\\\n# Extract features from the image\\\\nfeatures = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'A pre-trained deep learning model that extracts features from images using the MobileNetV2 architecture.\\'}', metadata={})]", "category": "generic"} {"question_id": 633, "text": " Analyze the similarity between movie reviews, obtain the embeddings for a given list of sentences so we can compare them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 634, "text": " Develop a mobile app to differentitate various car models from their photographs.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 635, "text": " In order to recommend news articles to users based on their interests, I need to identify similar articles to their preferences. Can you help me obtain the text embeddings for comparison?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 636, "text": " Create a program that can load an image from a web store and recognize the type of product it is.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.zeros([1, 224, 224, 3])\\\\noutput = model(image)\\\\nprint(output)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\\'}', metadata={})]", "category": "generic"} {"question_id": 637, "text": " Identify a suitable vector representation for the given sentence to be used later for semantic textual similarity tasks.\\n###Input: \\\"I am at the airport waiting for my flight.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Provides a feature vector for images\\', \\'api_name\\': \\'image_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\", \\'api_arguments\\': [\\'image_input\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\\')\\\\nfeature_vector = model(image_input)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'This API provides a feature vector for images using TensorFlow Hub and the Inception V3 model. It can be used for various image-related tasks such as classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 638, "text": " I need to match new customers' feedbacks to the closest existing feedbacks to avoid duplicating work. Help me obtain an embedding for each customer feedback.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL of the pre-trained model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=(224, 224))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Make predictions\\\\npredictions = model(input_image)\\\\n\\\\n# Decode the predictions\\\\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)\\\\nprint(class_names)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'This API loads a pre-trained image classification model from TensorFlow Hub, allowing users to classify images using the loaded model. The example demonstrates how to load a MobileNetV2 model, preprocess an input image, make predictions, and decode the predictions to obtain class names.\\'}', metadata={})]", "category": "generic"} {"question_id": 639, "text": " We have images of cars, and we want to extract their features and then create a classifier to classify them based on their make and model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 640, "text": " I am trying to build a mobile app to identify different food items. Can you suggest an effective pre-trained model for that purpose?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 641, "text": " Classify images of different dog breeds to find the most suitable breed for our pet store.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained model for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [{\\'model_id\\': \\'string (URL or local path to the model)\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load and preprocess an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\n# Classify the image\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\\\\nprint(\\'Predicted class:\\', predicted_class)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained model for image classification using TensorFlow Hub.\\'}', metadata={})]", "category": "generic"} {"question_id": 642, "text": " We are organizing an event in a park and we want to monitor the crowd by detecting the number of people in the area.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the TensorFlow Hub model URL\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\n\\\\n# Detect objects in the image\\\\nresult = model(image)\\\\n\\\\n# Print detected objects\\\\nfor obj in result[\\'detection_boxes\\']:\\\\n print(obj)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\\'}', metadata={})]", "category": "generic"} {"question_id": 643, "text": " Analyze a video for content and classify it into an appropriate category.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 644, "text": " Estimate semantic similarity of two movie descriptions for a recommendation system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 645, "text": " I want to predict breed of dogs among 10 different breeds using images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 646, "text": " Imagine you are developing an app integrated with augmented reality that develops a customized filter based on what the user points their camera to. You need to extract features from images to create this filter.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 647, "text": " Analyze an image of a cat and extract useful information that can help classify it with other animals.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 648, "text": " Analyze customer support email messages and get an idea about their satisfaction by generating embeddings for each email message.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for text\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\'])\", \\'performance\\': {\\'dataset\\': \\'Various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'A pre-trained model for generating text embeddings using the Universal Sentence Encoder. It can be used for various NLP tasks like semantic similarity, text classification, and clustering.\\'}', metadata={})]", "category": "generic"} {"question_id": 649, "text": " Can you help me convert the following sentence into a numerical representation? \\\"The quick brown fox jumps over the lazy dog.\\\"\\n###Input: \\\"The quick brown fox jumps over the lazy dog.\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'url: https://tfhub.dev/google/universal-sentence-encoder/4\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\", \\'performance\\': {\\'dataset\\': \\'Various\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A text preprocessing API that loads the Universal Sentence Encoder model from TensorFlow Hub for generating sentence embeddings.\\'}', metadata={})]", "category": "generic"} {"question_id": 650, "text": " We want to recognize the objects present in an image. Automate the process using a suitable model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\nresult = detector(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'A pre-trained object detection model that can detect multiple objects in an image. The model is based on the SSD Mobilenet V2 architecture and is trained on the COCO dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 651, "text": " Create a system to verify if different product images in our ecommerce store belong to the same category.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 652, "text": " I have an image of a cake and want to know if the model can correctly identify it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 653, "text": " I need to automatically organize a collection of product descriptions by similarity. Can you please generate embeddings for these descriptions?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate embeddings for audio files\\', \\'api_name\\': \\'audio_embedding_model\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\", \\'api_arguments\\': {\\'audio_inputs\\': \\'Tensor of shape [batch_size, num_samples] containing audio signals\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/yamnet/1\\')\\\\n\\\\n# Load an audio file\\\\naudio_data = ... # Load audio data as a tensor\\\\n\\\\n# Generate embeddings\\\\nembeddings = model(audio_data)\", \\'performance\\': {\\'dataset\\': \\'YAMNet\\', \\'accuracy\\': \\'81.6% top-1 accuracy on AudioSet\\'}, \\'description\\': \\'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 654, "text": " I need to implement a classifier using MobileNetV2 to label the content of an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image segmentation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image segmentation model\\', \\'api_name\\': \\'deeplabv3_257_mv_gpu\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \\'import tensorflow_hub as hub\\\\nmodel = hub.load(\"https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\")\\\\nsegmentation = model(input_image)\\', \\'performance\\': {\\'dataset\\': \\'PASCAL VOC 2012\\', \\'accuracy\\': \\'0.897\\'}, \\'description\\': \\'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\\'}', metadata={})]", "category": "generic"} {"question_id": 655, "text": " I am trying to develop an app to classify various breeds of cats and dogs. Can you suggest me any pre-trained model that I can use for this purpose?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\npredicted_class = tf.argmax(predictions[0])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 656, "text": " I have a set of images from a recent trip, and I'd like to automatically classify the most prominent subject in each photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 657, "text": " A sports broadcaster wants to automatically detect different players on the field during a soccer match.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object Detection\\', \\'api_name\\': \\'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/1\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/1\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/1\\')\\\\nimage_tensor = tf.convert_to_tensor(image_np, dtype=tf.uint8)\\\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\\\noutput_dict = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'COCO 2017\\', \\'accuracy\\': \\'30.9 mAP\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using Single Shot MultiBox Detector (SSD) with MobileNet V2 FPN-Lite 320x320 as the feature extractor. The model is trained on the COCO 2017 dataset and can detect 80 different object classes.\\'}', metadata={})]", "category": "generic"} {"question_id": 658, "text": " Develop a text classifier system that can categorize sentences into multiple categories, such as sentiment analysis, topic categorization, etc.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world\\'])\", \\'performance\\': {\\'dataset\\': \\'STS benchmark\\', \\'accuracy\\': \\'Pearson correlation coefficient of 0.78\\'}, \\'description\\': \\'A pre-trained model for embedding text into high-dimensional vectors that can be used for a wide range of tasks such as sentiment analysis, semantic similarity, and classification.\\'}', metadata={})]", "category": "generic"} {"question_id": 659, "text": " I am comparing sneakers in a catalog, and I want to extract the image features from the sneakers' images to cluster them.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'(224, 224, 3)\\', \\'output_shape\\': \\'(1280,)\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=(224, 224, 3),\\\\n output_shape=(1280,))\\\\n])\\\\n\\\\n# Use the model to extract features from an image\\\\nfeature_vector = model.predict(image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.8%\\'}, \\'description\\': \\'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 660, "text": " I have an application that recognizes objects in images, and I need a pre-trained model to perform the classification task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using a pre-trained model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id: The ID of the pre-trained model to be used for image classification\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Load an image and preprocess it\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\\\n\\\\n# Make a prediction\\\\npredictions = model(tf.expand_dims(image, 0))\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API allows users to perform image classification using a pre-trained TensorFlow Hub model. The model can be loaded using the model_id, and the API provides functionality for loading and preprocessing images, as well as making predictions.\\'}', metadata={})]", "category": "generic"} {"question_id": 661, "text": " Recommend me an API that could classify thousands of emails into categories: office, financial, social, commercial, travel.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\n# Example: Load an image and classify it\\\\n# image = ... # Load an image\\\\n# result = model(image)\\\\n# print(result)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not provided\\'}, \\'description\\': \\'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\\'}', metadata={})]", "category": "generic"} {"question_id": 662, "text": " Let's build an image classifier for recognizing dog breeds using the pre-trained MobileNet V2 model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained model for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'2.x\\', \\'tensorflow_hub\\': \\'0.12.0\\'}, \\'example_code\\': \"import tensorflow_hub as hub\\\\nimport tensorflow as tf\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\ninput_image = tf.zeros([1, 224, 224, 3])\\\\npredictions = model(input_image)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'This model is a pre-trained image classifier using MobileNet V2 architecture on ImageNet dataset with 100% depth and 224x224 input size. It is optimized for performance on mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 663, "text": " My son has taken several pictures of his toys and he wants to know what are those toys by using a model that classifies images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 664, "text": " My client wants to build an application for identifying flower species using machine learning. How can we achieve that?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embed text data for machine learning models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\')\", \\'api_arguments\\': {\\'model_id\\': \\'A string representing the URL or path to the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\\'\\\\nembed = hub.load(model_id)\\\\nembeddings = embed([\\'Hello, world!\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Wikipedia\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\\'}', metadata={})]", "category": "generic"} {"question_id": 665, "text": " Help me find the level of similarity between this song lyric and a list of other song lyrics by generating embeddings.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image Frame Interpolation\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Generate intermediate frames between two input frames\\', \\'api_name\\': \\'image-frame-interpolation\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\')\", \\'api_arguments\\': {\\'inputs\\': \\'A list of two input frames\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\ninputs = [frame1, frame2]\\\\ninterpolated_frame = hub.load(\\'https://tfhub.dev/google/two-frame-vgg/1\\').call(inputs)\", \\'performance\\': {\\'dataset\\': \\'Adobe240fps\\', \\'accuracy\\': \\'PSNR: 29.8 dB, SSIM: 0.902\\'}, \\'description\\': \\'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\\'}', metadata={})]", "category": "generic"} {"question_id": 666, "text": " Recommend a model to me that easily detects the type of plant in a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 667, "text": " Help me identify objects in an image that I load from my computer.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [{\\'name\\': \\'model_id\\', \\'type\\': \\'string\\', \\'description\\': \\'The ID of the model to load from TensorFlow Hub\\'}], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\n# Load an image, preprocess it, and run the detector\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.image.resize(input_image, (300, 300))\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\n# Run the detector\\\\noutput = detector(input_image)\\\\n\\\\n# Process the output\\\\nfor i in range(output[\\'num_detections\\']):\\\\n if output[\\'detection_scores\\'][i] > 0.5:\\\\n print(\\'Detected object:\\', output[\\'detection_class_entities\\'][i], \\'with confidence:\\', output[\\'detection_scores\\'][i])\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP@0.5IOU\\'}, \\'description\\': \\'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\\'}', metadata={})]", "category": "generic"} {"question_id": 668, "text": " I need to perform object detection to identify contents in some paintings for a museum project.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 669, "text": " Can you use the image classification model to get adequate feature for specific images on a smaller resolution mobile device?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading a pre-trained image classification model\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'model_id\\': \\'URL of the TensorFlow Hub model\\'}, \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'\\\\nmodel = hub.load(model_id)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Depends on the specific model\\'}, \\'description\\': \\'This API allows you to load a pre-trained image classification model from TensorFlow Hub for use in your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 670, "text": " I found some random birds image on the internet and now I want to classify it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'handle\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', \\'trainable\\': \\'Optional: Set to True if you want to fine-tune the model\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n trainable=False),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0% top-1 accuracy\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\\'}', metadata={})]", "category": "generic"} {"question_id": 671, "text": " I need a mechanism to detect and group photos of cars on my computer based on their make and model. Can you suggest an approach for this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Object detection\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\\'\\\\nloaded_model = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\')\\\\nimage_np = np.array(image)\\\\n\\\\ninput_tensor = tf.convert_to_tensor(image_np)\\\\ninput_tensor = input_tensor[tf.newaxis, ...]\\\\n\\\\noutput_dict = loaded_model(input_tensor)\\\\n\\\\nboxes = output_dict[\\'detection_boxes\\'][0].numpy()\\\\nscores = output_dict[\\'detection_scores\\'][0].numpy()\\\\nclasses = output_dict[\\'detection_classes\\'][0].numpy().astype(np.int32)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'0.320\\'}, \\'description\\': \\'This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores.\\'}', metadata={})]", "category": "generic"} {"question_id": 672, "text": " Retrieve features from images of site inspections which will further be used for inspection report categorization.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Extract features from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\',\\\\n input_shape=[224, 224, 3],\\\\n output_shape=[1280])\\\\n])\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_batch = tf.expand_dims(image_array, axis=0)\\\\n\\\\nfeatures = model.predict(image_batch)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image feature vector model based on MobileNetV2 architecture. It can be used to extract features from images, which can be used for various machine learning tasks like image classification, object detection, and more.\\'}', metadata={})]", "category": "generic"} {"question_id": 673, "text": " I'm interested in clustering similar images from my trip to Paris, could you please recommend a way to transform images before applying a clustering algorithm?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Load and use pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.random.uniform((1, 224, 224, 3))\\\\npredictions = model(image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\\'}, \\'description\\': \\'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\\'}', metadata={})]", "category": "generic"} {"question_id": 674, "text": " Help me identify the type of food in a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image object detection\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Detect objects in images\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\\'\\\\ndetector = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'image.jpg\\')\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\nresult = detector(input_image)\", \\'performance\\': {\\'dataset\\': \\'COCO\\', \\'accuracy\\': \\'mAP\\'}, \\'description\\': \\'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\\'}', metadata={})]", "category": "generic"} {"question_id": 675, "text": " I am trying to determine the sentiment of customer reviews to improve our product, help me convert these reviews into high-dimensional vectors.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text into high-dimensional vectors\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\', \\'I am learning about embeddings.\\'])\\\\nprint(embeddings)\", \\'performance\\': {\\'dataset\\': \\'Various tasks\\', \\'accuracy\\': \\'State-of-the-art\\'}, \\'description\\': \\'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 676, "text": " Generate a customer service bot and find the most suitable response for a user query from a list of responses.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.6.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\ndef predict(image):\\\\n logits = model(image)\\\\n return logits\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\\'}', metadata={})]", "category": "generic"} {"question_id": 677, "text": " How would you analyze the similarity of two job descriptions to check whether the positions are similar?\\n###Input: {\\\"text1\\\": \\\"We are looking for a highly skilled software engineer to join our team. The successful candidate will work on the development and implementation of a wide range of software solutions for our clients. Strong programming skills and excellent communication abilities are essential for this role.\\\", \\\"text2\\\": \\\"We are in search of a talented programmer to contribute to the design and creation of various software products for our customer base. The ideal candidate will possess outstanding coding skills along with solid communication skills to collaborate effectively with team members and clients.\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Image classification using pre-trained models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5\\')\", \\'api_arguments\\': {\\'model_id\\': \\'The URL of the TensorFlow Hub model or the path to the model on disk\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Varies depending on the model used\\'}, \\'description\\': \\'TensorFlow Hub provides a collection of pre-trained image classification models that can be easily loaded and used for various image classification tasks. These models are trained on the ImageNet dataset and can be fine-tuned for specific use cases.\\'}', metadata={})]", "category": "generic"} {"question_id": 678, "text": " My friends and I are outdoor enthusiasts and we would like an app that can identify the plants we encounter.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Pre-trained image classification models\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow-hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(299, 299))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\n\\\\npredictions = model(input_image)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'0.779\\'}, \\'description\\': \\'This API provides pre-trained image classification models using TensorFlow Hub for various architectures like Inception V3, MobileNet, and others. These models are trained on the ImageNet dataset and can be used for transfer learning or fine-tuning on custom datasets.\\'}', metadata={})]", "category": "generic"} {"question_id": 679, "text": " I want to analyze the sentiment of tweets about a company's product. To do that, generate embeddings for the input tweets.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/universal-sentence-encoder/4\\'\\\\nmodel = hub.load(model_id)\\\\nembeddings = model([\\'Hello World\\'])\", \\'performance\\': {\\'dataset\\': \\'n/a\\', \\'accuracy\\': \\'n/a\\'}, \\'description\\': \\'Load a TensorFlow Hub model for text preprocessing and generate embeddings for input text.\\'}', metadata={})]", "category": "generic"} {"question_id": 680, "text": " We want to analyze a collection of images for their features and create a summary of their distinct characteristics.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'tensorflow_hub.KerasLayer\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/nnlm-en-dim128/2\\')\", \\'api_arguments\\': {\\'handle\\': \\'A string, the handle of the Hub module to load\\', \\'kwargs\\': \\'Optional keyword arguments\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.8.0\\'}, \\'example_code\\': \\'import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\\\\n])\\', \\'performance\\': {\\'dataset\\': \\'NNLM\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\\'}', metadata={})]", "category": "generic"} {"question_id": 681, "text": " We just took many different images of the front of a house and want to isolate the windows part from the whole image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading pre-trained models for image classification\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_classification\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\", \\'api_arguments\\': \\'URL of the pre-trained model\\', \\'python_environment_requirements\\': \\'TensorFlow 2.x, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\\')\\\\n\\\\n# Load an image and preprocess it\\\\ndef load_image(file_path):\\\\n img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\\\n img_array = tf.keras.preprocessing.image.img_to_array(img)\\\\n img_array = tf.expand_dims(img_array, 0) # Create a batch\\\\n return img_array\\\\n\\\\n# Predict the class of the image\\\\nimg_array = load_image(\\'path/to/your/image.jpg\\')\\\\npredictions = model(img_array)\\\\n\\\\n# Get the class with the highest probability\\\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\\'}', metadata={})]", "category": "generic"} {"question_id": 682, "text": " We have been receiving user-generated content that is sometimes not safe for work. We would like to develop a system to classify such images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading and using pre-trained models for image classification\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0\\', \\'tensorflow_hub\\': \\'>=0.12\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\\'\\\\nmodel = hub.load(model_id)\\\\n\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/image.jpg\\', target_size=(224, 224))\\\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\\\nimage_array = tf.expand_dims(image_array, 0)\\\\n\\\\npredictions = model(image_array)\\\\n\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'94.1%\\'}, \\'description\\': \\'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\\'}', metadata={})]", "category": "generic"} {"question_id": 683, "text": " My nephew is passionate about animals, and I want to create an app that tells him the type of animal in a picture.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Create image feature vector\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\\'\\\\nmodel = hub.load(model_id)\\\\nfeature_vector = model(image_tensor)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'Not specified\\'}, \\'description\\': \\'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\\'}', metadata={})]", "category": "generic"} {"question_id": 684, "text": " I have an image of a dog breed, and I want to know the breed.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet-mobilenet_v2_100_224-feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'input_shape\\': \\'[224, 224, 3]\\', \\'output_shape\\': \\'[1280]\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"model = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\', input_shape=[224, 224, 3], output_shape=[1280]),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.9%\\'}, \\'description\\': \\'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\\'}', metadata={})]", "category": "generic"} {"question_id": 685, "text": " How to identify dog breed through a mobile app by taking a dog picture as they walk?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image feature vector\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Feature extraction from images\\', \\'api_name\\': \\'imagenet_mobilenet_v2_100_224_feature_vector\\', \\'api_call\\': \"hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\')\", \\'api_arguments\\': {\\'url\\': \\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'}, \\'python_environment_requirements\\': {\\'tensorflow\\': \\'>=2.0.0\\', \\'tensorflow_hub\\': \\'>=0.12.0\\'}, \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\n\\\\nmodel = tf.keras.Sequential([\\\\n hub.KerasLayer(\\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\\'),\\\\n tf.keras.layers.Dense(num_classes, activation=\\'softmax\\')\\\\n])\\\\n\\\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\\\n loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\\\n metrics=[\\'accuracy\\'])\\\\n\\\\nmodel.fit(x_train, y_train, epochs=5)\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'71.0%\\'}, \\'description\\': \\'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\\'}', metadata={})]", "category": "generic"} {"question_id": 686, "text": " My friend has taken a photo of a plant, and I want to know what species it is.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Image classification\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Loading\\', \\'api_name\\': \\'model_id\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\')\", \\'api_arguments\\': [\\'model_id\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nmodel_id = \\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\\'\\\\nmodel = hub.load(model_id)\\\\nimage = tf.keras.preprocessing.image.load_img(\\'path/to/your/image.jpg\\', target_size=(299, 299))\\\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\\\ninput_image = tf.expand_dims(input_image, 0)\\\\npredictions = model(input_image)\\\\npredicted_class = tf.argmax(predictions[0]).numpy()\", \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'78.1%\\'}, \\'description\\': \\'This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image. The model has been trained on the ImageNet dataset and achieves 78.1% accuracy.\\'}', metadata={})]", "category": "generic"} {"question_id": 687, "text": " Can you help me to find similarity between movie plot descriptions, so that I can recommend similar movies?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text embedding\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Embedding text data\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': \\'Text input\\', \\'python_environment_requirements\\': \\'TensorFlow, TensorFlow Hub\\', \\'example_code\\': \"import tensorflow as tf\\\\nimport tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello, world!\\', \\'How are you?\\'])\", \\'performance\\': {\\'dataset\\': \\'Various text sources\\', \\'accuracy\\': \\'High similarity scores for semantically similar sentences\\'}, \\'description\\': \\'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\\'}', metadata={})]", "category": "generic"} {"question_id": 688, "text": " Design a natural language processing model using text tokenization and vectorization. I need some preprocessing before dealing with classifiers.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text preprocessing\\', \\'framework\\': \\'TensorFlow Hub\\', \\'functionality\\': \\'Text tokenization and vectorization\\', \\'api_name\\': \\'universal-sentence-encoder\\', \\'api_call\\': \"hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\", \\'api_arguments\\': [\\'input_text\\'], \\'python_environment_requirements\\': [\\'tensorflow\\', \\'tensorflow_hub\\'], \\'example_code\\': \"import tensorflow_hub as hub\\\\nembed = hub.load(\\'https://tfhub.dev/google/universal-sentence-encoder/4\\')\\\\nembeddings = embed([\\'Hello world!\\'])\", \\'performance\\': {\\'dataset\\': \\'Various\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is a pre-trained model available on TensorFlow Hub.\\'}', metadata={})]", "category": "generic"}