 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
 Use this API documentation for reference:  {"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."}}


 My friend sent me a picture of an animal and I want to find out the type of animal in the picture.\n
 Use this API documentation for reference:  {"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"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\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, 0)\npredictions = model(image_batch)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "Load and use a pre-trained model from TensorFlow Hub for image classification"}}


 Summarize the article and identify the key topics discussed.\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the model_id"}}


 Help me identify what's in a photo using a model that is pre-trained on the ImageNet dataset.\n
 Use this API documentation for reference:  {"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.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", "output_shape": [1001], "input_shape": [224, 224, 3], "trainable": false}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained MobileNetV2 model for image classification on ImageNet dataset with 224x224 input size and 1001 output classes."}}


 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
 Use this API documentation for reference:  {"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": "List of strings"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "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!', 'My name is John Doe'])", "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."}}


 I am trying to find objects in an image with high accuracy. Help me use a pre-trained model to detect objects.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024/1')", "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/centernet/hourglass_1024x1024/1'\ndetector = hub.load(model_id)\n# Load an image\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg')\n# Process the image\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.image.resize(input_image, (1024, 1024))\ninput_image = tf.expand_dims(input_image, axis=0) / 255.0\n# Detect objects\nresults = detector(input_image)\n# Print the results\nprint(results)", "performance": {"dataset": "COCO", "accuracy": "mAP@0.5IOU"}, "description": "A pre-trained TensorFlow Hub model for detecting objects in images using the CenterNet architecture with an Hourglass backbone."}}


 I have a fashion catalog and I want to find visually similar clothes from the catalog using the images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extracting 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": "[batch_size, 224, 224, 3]", "output_shape": "[batch_size, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n# Train and evaluate the model\n# model.fit(train_data, train_labels, epochs=5)\n# model.evaluate(test_data, test_labels)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture, which is optimized for low-latency and small size, while maintaining competitive accuracy on the ImageNet dataset."}}


 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
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained models", "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": {"model_url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\ninput_arr = tf.keras.preprocessing.image.img_to_array(image)\ninput_arr = np.array([input_arr])\npredictions = model.predict(input_arr)", "performance": {"dataset": "ImageNet", "accuracy": "94.1%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture with 130 depth multiplier and 224x224 input size. The model has been trained on the ImageNet dataset and can be used for classifying images into 1000 different categories."}}


 Could you identify the object in an image? Which pre-trained model can I use?\n
 Use this API documentation for reference:  {"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/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf, tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')\ninput_image = tf.zeros([1, 224, 224, 3])\noutput = model(input_image)", "performance": {"dataset": "ImageNet", "accuracy": "Top-1: 80.9%, Top-5: 95.5%"}, "description": "A pre-trained model for image classification using MobileNetV2 architecture, trained on ImageNet dataset. The model is optimized for low-latency and small model size, suitable for mobile and edge devices."}}


 Find out what an image is about by using a pre-trained image classification model, considering its contents.\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the provided model_id."}}


 Develop a model for product categories from images I provide.\n
 Use this API documentation for reference:  {"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": {"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\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    tf.keras.layers.Dense(1, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n# Train the model with your data\nmodel.fit(x_train, y_train, epochs=5, batch_size=32)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset. It can be used for image classification, object detection, and other computer vision tasks."}}


 Can you please provide an API that will help us find semantic similarity between multiple sentences?\n
 Use this API documentation for reference:  {"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!', 'How are you?'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "pearson_correlation=0.803"}, "description": "A model that encodes text into 512-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks"}}


 Describe an object in an image.\n
 Use this API documentation for reference:  {"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/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('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = model(image)\n", "performance": {"dataset": "ImageNet", "accuracy": "77.9%"}, "description": "A pre-trained model for image classification using Inception V3 architecture and trained on ImageNet dataset."}}


 I have a collection of paintings and I'm looking for a way to generate a list of their most common features.\n
 Use this API documentation for reference:  {"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),\n                    trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\n              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model that takes an image input and outputs a 1280-dimensional feature vector representing the image, which can be used for various tasks such as classification, clustering, and similarity search."}}


 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
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained 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": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.11.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "performance": {"dataset": "ImageNet", "accuracy": "0.919"}, "description": "This module uses the MobileNet v2 architecture for image classification, pre-trained on the ImageNet dataset. The model accepts image input of size 224x224 pixels and outputs a 1001-element vector of logits."}}


 Can you help extract meaning from a list of sentences by converting them into high-dimensional vectors?\n
 Use this API documentation for reference:  {"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!']); print(embeddings)", "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."}}


 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
 Use this API documentation for reference:  {"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": "embeddings = embed(['Hello world'])", "performance": {"dataset": "STS Benchmark", "accuracy": "Mean Squared Error (MSE) of 0.30 on the development set"}, "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."}}


 I want a tool to help me count how many cars are parked in our parking lot.\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'\nloaded_model = hub.load(model_id)\nimage = tf.io.read_file('image.jpg')\ndecoded_image = tf.image.decode_jpeg(image)\nresized_image = tf.image.resize(decoded_image, [640, 480])\ninput_image = tf.expand_dims(resized_image, 0)\nresult = loaded_model(input_image)", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "A TensorFlow Hub model for detecting objects in images using the SSD MobileNet V2 architecture."}}


 Can you help me classify the species of a bird that I have in my photo? \n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained models", "api_name": "imagenet_mobilenet_v2", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"module_url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4", "input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1001]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\")\n])\n# Prepare an image\nimage = tf.keras.preprocessing.image.load_img(\"path/to/your/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# Make a prediction\npredictions = model.predict(input_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset. It can be used for classifying images into 1000 different categories."}}


 Can you help me analyze the contents of a photograph?\n
 Use this API documentation for reference:  {"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/2')", "api_arguments": {"model_id": "string"}, "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/google/tf2-preview/mobilenet_v2/classification/2')", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "Load a pre-trained image classification model from TensorFlow Hub, providing a model_id for the desired model."}}


 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
 Use this API documentation for reference:  {"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/4')", "api_arguments": {"model_id": "string"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\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# Make a prediction\nprediction = model(image_array)\n# Decode the prediction\ndecoded_prediction = tf.keras.applications.mobilenet_v2.decode_predictions(prediction.numpy())", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "Pre-trained image classification model using MobileNetV2 architecture on the ImageNet dataset."}}


 Analyze various photograps and determine what objects each one contains.\n
 Use this API documentation for reference:  {"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/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\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\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)\npredictions = model(input_image)\n", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "A pre-trained image classification model using Inception V3 architecture, trained on ImageNet dataset. The model can be used to classify images into 1000 different categories."}}


 Determine the type of animal we found during the hike. It's important to know if it could be dangerous.\n
 Use this API documentation for reference:  {"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/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('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)\npredictions = model(input_image)\n", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "A pre-trained model for image classification using Inception V3 architecture, available on TensorFlow Hub. The model has been trained on the ImageNet dataset and can be used to classify images into 1000 different classes."}}


 Expedia wants to recommend hotels based on the image of their rooms. They need a way to extract features from these images.\n
 Use this API documentation for reference:  {"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."}}


 Automate the process of training a deep learning model to distinguish between images of cats and dogs.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract feature vector 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": ">=0.8.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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(1, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a pre-trained model to extract feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. It can be used for various image classification tasks and transfer learning."}}


 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
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generating 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": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": {"import": ["import tensorflow as tf", "import tensorflow_hub as hub"], "model": ["model = tf.keras.Sequential([", "  hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',", "                 input_shape=(224,224,3),", "                 trainable=False),", "  tf.keras.layers.Dense(100, activation='softmax')", "])"]}, "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained deep learning model for generating image feature vectors using MobileNetV2 architecture with 100% depth and 224x224 input size."}}


 I need a model that can classify different architectural styles in images.\n
 Use this API documentation for reference:  {"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": "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(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This module uses the MobileNet v2 architecture for extracting image features. It takes an image input of shape (224, 224, 3) and outputs a 1280-dimensional feature vector."}}


 Provide me a pre-trained model to classify imported images.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": [{"name": "model_id", "type": "string", "description": "The ID of the pre-trained model to be loaded"}], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the specified model ID."}}


 I'd like to automatically classify food images at a grocery store to add labels to the products.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models for image classification", "api_name": "mobilenet_v2", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "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\nmodel = hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')\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)\npredictions = model(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for image classification using TensorFlow Hub"}}


 Analyze a collection of images to help me recognize my dog's breed.\n
 Use this API documentation for reference:  {"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": {"image_size": 224, "input_shape": [224, 224, 3], "output_shape": [1280]}, "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([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224,224,3))\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.expand_dims(image, 0)\n# Extract feature vector\nfeature_vector = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture and trained on ImageNet dataset. It can be used for feature extraction and fine-tuning on new classification tasks."}}


 Create a feature vector from an image using a pre-trained model for use in an image similarity search.\n
 Use this API documentation for reference:  {"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),\n                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a feature vector for an image using the MobileNet V2 architecture trained on the ImageNet dataset. The feature vector can be used as input for training other models or for image similarity tasks."}}


 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
 Use this API documentation for reference:  {"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."}}


 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
 Use this API documentation for reference:  {"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'])", "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. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs."}}


 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
 Use this API documentation for reference:  {"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": ["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')\ntext = ['This is a sample text']\nembeddings = embed(text)\nprint(embeddings)", "performance": {"dataset": "SNLI", "accuracy": "86.9%"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for a wide range of tasks such as semantic similarity, text classification, clustering, and sentiment analysis."}}


 We need recommendations for similar restaurants. Calculate the embedding of restaurant descriptions to create a list of the most similar establishments.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Text preprocessing module", "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!', 'How are you?'])", "performance": {"dataset": "Multiple", "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."}}


 Convert a book summary into a high-dimensional vector for determining similarity to another book's summary.\n
 Use this API documentation for reference:  {"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": ["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(['text to be encoded'])", "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."}}


 Tell me how to convert post titles into numerical vectors so that I can find similar titles later on.\n
 Use this API documentation for reference:  {"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."}}


 We want to analyze security camera footage and determine if there are any people in the frame.\n
 Use this API documentation for reference:  {"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."}}


 I took a picture of my park today. I want to know what kinds of objects I captured in the picture.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection and localization", "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": "mAP@0.5IOU"}, "description": "A pre-trained object detection model that can detect and localize objects in an image using TensorFlow Hub."}}


 Our application needs to identify objects in an image. Extract them using pre-trained model.\n
 Use this API documentation for reference:  {"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."}}


 Compare the similarity of two customer reviews for a product and see if they are positive or negative.\n
 Use this API documentation for reference:  {"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!'])", "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 a wide range of tasks such as semantic similarity, text classification, clustering, and other natural language processing tasks."}}


 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
 Use this API documentation for reference:  {"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')", "api_arguments": "input_shape, input_tensor, trainable, output_shape", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5',\n                   input_shape=(299, 299, 3),\n                   trainable=False)\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(299, 299))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "78.1%"}, "description": "A pre-trained model that extracts feature vectors from images using Inception V3 architecture"}}


 I want to compare the meaning similarity of two sentences. Find a way to encode the sentences and calculate their similarity.\n
 Use this API documentation for reference:  {"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 that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 My retail client wishes to recognize when a doorbell is rang in audio files of their store.\n
 Use this API documentation for reference:  {"domain": "Audio event classification", "framework": "TensorFlow Hub", "functionality": "Classify audio events", "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": "", "performance": {"dataset": "", "accuracy": ""}, "description": "A TensorFlow Hub model for classifying audio events."}}


 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
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "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": {"input_image": "A 224x224x3 image tensor", "output": "A 1280-dimensional feature vector"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\nimage_tensor = tf.zeros([1, 224, 224, 3]) # Replace with your own image tensor\nfeatures = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model based on MobileNetV2, designed for efficient on-device image classification and transfer learning. It takes a 224x224x3 image tensor as input and outputs a 1280-dimensional feature vector."}}


 I need to know what is in this photo I took. Can you tell me?\n###Input: {\"image\": \"path/to/image.jpg\"}\n
 Use this API documentation for reference:  {"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_130_224/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/mobilenet_v2_130_224/classification/4'\nmodel = hub.load(model_id)\n# Load and preprocess image\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# Predict\npredictions = model(image_array)\n# Decode predictions\nclass_names = imagenet_labels()\npredicted_class = class_names[np.argmax(predictions[0])]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 Accuracy: 80.9%, Top-5 Accuracy: 95.2%"}, "description": "A pre-trained image classification model based on the MobileNetV2 architecture, fine-tuned on the ImageNet dataset. The model can be used to classify images into 1000 different categories."}}


 Identify objects in an image to let me know what objects are in the image.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "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": "(224, 224, 3)", "output_shape": "(1024)", "trainable": "False"}, "python_environment_requirements": {"tensorflow": "2.0+", "tensorflow_hub": "0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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(10, activation='softmax')\n])\nmodel.summary()", "performance": {"dataset": "ImageNet", "accuracy": "70.9%"}, "description": "A pre-trained image feature extraction model using MobileNet architecture trained on ImageNet dataset. It can be used for transfer learning and fine-tuning on new image classification tasks."}}


 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: \"<path_to_image>\"\n
 Use this API documentation for reference:  {"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/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('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = model(image)\n", "performance": {"dataset": "ImageNet", "accuracy": "N/A"}, "description": "Load and use a pre-trained image classification model from TensorFlow Hub to classify images."}}


 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
 Use this API documentation for reference:  {"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"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nloaded_model = hub.load(model_id)\n# Load an image\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, 0)\n# Run the model\npredictions = loaded_model(image_batch)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API allows users to load pre-trained models for image classification from TensorFlow Hub. The loaded models can be used to classify images into one of the pre-defined categories."}}


 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
 Use this API documentation for reference:  {"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!']); print(embeddings)", "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."}}


 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
 Use this API documentation for reference:  {"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."}}


 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
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2-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\nmodel = 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])\nfeatures = model.predict(images)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset. It can be used for feature extraction from images."}}


 I want to compare the semantic similarity between two sentences. Give me the embeddings using pre-trained models.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed 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 as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\ntext = ['This is a test sentence.']\nembeddings = embed(text)", "performance": {"dataset": "STS Benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder (USE) is a pre-trained model for generating embeddings of text. It is trained on a variety of data sources and can be used for tasks such as semantic similarity and classification."}}


 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
 Use this API documentation for reference:  {"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": ["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!', 'How are you?'])", "performance": {"dataset": "STS-Benchmark", "accuracy": "Pearson correlation of 0.803"}, "description": "The Universal Sentence Encoder encodes text data into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 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
 Use this API documentation for reference:  {"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\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])\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."}}


 When creating a mobile application that uses image recognition, it should be able to determine what items are in the photo.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained model", "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": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0", "numpy": ">=1.19.5", "PIL": ">=8.3.2"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nfrom PIL import Image\nimport numpy as np\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n])\nimage = Image.open('path/to/your/image.jpg').resize((224, 224))\ninput_image = np.array(image) / 255.0\ninput_image = np.expand_dims(input_image, axis=0)\npredictions = model.predict(input_image)\npredicted_class = np.argmax(predictions[0])\nprint('Predicted class:', predicted_class)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset with 100% depth and 224x224 input size."}}


 I have some images and I want to compare them efficiently. Extract the features of the images so I can perform similarity analysis.\n
 Use this API documentation for reference:  {"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"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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])\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)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a Keras layer that extracts features from images using the MobileNetV2 model trained on ImageNet dataset. It can be used for various tasks such as image classification, object detection, and transfer learning."}}


 Manage the camera in the smartphone to recognize multiple objects during a forest walk.\n
 Use this API documentation for reference:  {"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\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')\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# 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"}}


 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
 Use this API documentation for reference:  {"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": {"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": "", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with 100% depth and 224x224 input size."}}


 We want to help users identify the species of a bird in a given picture.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained model 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/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "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_130_224/classification/4')\n# Load an image and preprocess it\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# Make predictions\npredictions = model(input_image)\n# Get the top predicted class\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1: 80.9%, Top-5: 95.2%"}, "description": "A pre-trained MobileNetV2 model for image classification on the ImageNet dataset."}}


 I want to analyze a list of customer reviews to gain insight into their sentiment.\n
 Use this API documentation for reference:  {"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."}}


 Gather high-dimensional embeddings of some scientific abstracts to make a similarity-based network analysis.\n
 Use this API documentation for reference:  {"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": ["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!', 'How are you?'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "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."}}


 Use a pre-trained model to find out traits that are similar to buildings in images.\n
 Use this API documentation for reference:  {"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\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# 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# 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."}}


 I need to measure the semantic similarity between pairs of sentences. Help me to get the embeddings of the sentences.\n
 Use this API documentation for reference:  {"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."}}


 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
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"feature_vector_url": "URL of the pre-trained model", "input_image": "Input image tensor"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nfeature_vector_url = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4'\ninput_image = tf.keras.Input(shape=(224, 224, 3))\nfeatures = hub.KerasLayer(feature_vector_url)(input_image)\nmodel = tf.keras.Model(inputs=input_image, outputs=features)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "This API allows you to extract image feature vectors using TensorFlow Hub's pre-trained models. The feature vectors can be used in various applications, such as image classification, object detection, and image similarity."}}


 We are building an AI app that can help us determine what kind of food is in a picture.\n
 Use this API documentation for reference:  {"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/inception_v3/classification/5')", "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\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\n# Load and preprocess an image\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.expand_dims(image, axis=0)\n# Make predictions\npredictions = model(image)\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1: 77.9%, Top-5: 93.7%"}, "description": "A pre-trained image classification model using Inception V3 architecture, trained on ImageNet dataset."}}


 Categorize images captured from a drone in different landscapes like forests, rivers, and mountains.\n
 Use this API documentation for reference:  {"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.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", "output_shape": [1001], "input_shape": [224, 224, 3], "trainable": false}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained MobileNetV2 model for image classification on ImageNet dataset with 224x224 input size and 1001 output classes."}}


 I am planning to sort and label my photo collection. Help me identify different objects in my master album.\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\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.expand_dims(image, 0)\npredictions = model(image)\nprint(predictions)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub and use it to predict the class of an input image."}}


 Determine the dominant object in an image by extracting features from the image.\n
 Use this API documentation for reference:  {"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": ">=0.8.0"}, "example_code": "model = tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=[224,224,3], trainable=False)])", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "This module uses the MobileNet_v2_100_224 architecture to extract image features. It takes an input image of shape [224, 224, 3] and outputs a feature vector of shape [1280]."}}


 Estimate the similarity between two news headlines using pre-trained text embedding.\n
 Use this API documentation for reference:  {"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": ["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": "0.78 (Spearman correlation)"}, "description": "The Universal Sentence Encoder (USE) is a pre-trained model that encodes text into high-dimensional vectors. It is useful for various natural language processing tasks, such as text classification, semantic similarity, and clustering."}}


 I want to build a simple mobile app that identifies plants by analyzing photos of their leaves.\n
 Use this API documentation for reference:  {"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": {"model_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\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\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.expand_dims(image, 0)\npredictions = model(image)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture, trained on ImageNet dataset, with 100% depth and 224x224 input size."}}


 I want to make a food identification app that can help me classify different dishes at a restaurant.\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5'\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)\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."}}


 I want to know what is in this image I just took.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": {"model_id": "string"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "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)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "Load a pre-trained image classification model from TensorFlow Hub and use it to classify images."}}


 We need to classify images taken by our surveillance camera to detect unwanted activity.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "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"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained image classification model available on TensorFlow Hub, capable of classifying images into a large number of categories."}}


 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
 Use this API documentation for reference:  {"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": {"image": "A tensor of shape [1, height, width, 3] containing a single image"}, "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/faster_rcnn/resnet50_v1_640x640/1')\nimage_tensor = tf.convert_to_tensor(image, dtype=tf.float32)\ndetections = model(image_tensor)", "performance": {"dataset": "COCO 2017", "accuracy": "37.1 mAP"}, "description": "A pre-trained Faster R-CNN model with ResNet-50 backbone for object detection in images"}}


 How do I know the breed of a random dog in a picture?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained model", "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": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\ninput_array = tf.expand_dims(input_array, 0)\ninput_array /= 255.0\n# Make predictions\npredictions = model(input_array)\n# Decode predictions to class names\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy())", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture and trained on ImageNet dataset."}}


 I have a picture of my room and I want to identify all the objects present in it.\n
 Use this API documentation for reference:  {"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 unique identifier for the pre-trained model"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12.0"}, "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'))\nimage = tf.image.resize(image, (300, 300))\nimage = tf.expand_dims(image, 0)\nresults = detector(image)\nprint(results['detection_boxes'])\nprint(results['detection_classes'])\nprint(results['detection_scores'])", "performance": {"dataset": "COCO", "accuracy": "mAP (mean average precision)"}, "description": "A pre-trained model for detecting objects in images using TensorFlow Hub"}}


 I have a collection of movie descriptions and I want to find the resemblance between them.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data for machine learning tasks", "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')\ntext_input = ['This is a sample text.']\nembeddings = embed(text_input)", "performance": {"dataset": "STS benchmark", "accuracy": "85.1%"}, "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."}}


 To find similar images in a database, we need to extract useful features to compare images.\n
 Use this API documentation for reference:  {"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\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# Example input: a batch of images with shape [batch_size, 224, 224, 3].\nimages = tf.random.uniform((4, 224, 224, 3))\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."}}


 Which API could be used to compare the semantic similarity between two given pieces of text?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Create embeddings for text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of strings (sentences)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'Embedding with TensorFlow Hub'])", "performance": {"dataset": "STS Benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder (USE) is a pre-trained model that encodes text into high-dimensional vectors, which can be used for various natural language processing tasks."}}


 We have some cars pictures and want to classify them, give us the API call to get a pre-trained model.\n
 Use this API documentation for reference:  {"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/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf, tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')\ninput_image = tf.zeros([1, 224, 224, 3])\noutput = model(input_image)", "performance": {"dataset": "ImageNet", "accuracy": "Top-1: 80.9%, Top-5: 95.5%"}, "description": "A pre-trained model for image classification using MobileNetV2 architecture, trained on ImageNet dataset. The model is optimized for low-latency and small model size, suitable for mobile and edge devices."}}


 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
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data for machine learning tasks", "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": ">=0.11.0"}, "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nsentences = ['Hello, world!', 'Embeddings are fun!']\nembeddings = embed(sentences).numpy()", "performance": {"dataset": "STS Benchmark", "accuracy": "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."}}


 Help me convert Reddit post comments into numerical vector format that can be easily understood by machine algorithms.\n
 Use this API documentation for reference:  {"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": {"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(['sample sentence'])", "performance": {"dataset": "Various datasets", "accuracy": "Not mentioned"}, "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."}}


 Organize a digital library of photos and identify the ones with similar content by comparing their image features.\n
 Use this API documentation for reference:  {"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", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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])\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)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API extracts feature vectors from images using the MobileNetV2 model pretrained on the ImageNet dataset. It can be used for tasks like image classification, object detection, and image similarity."}}


 Given a collection of images, find the most likely object depicted in each image.\n
 Use this API documentation for reference:  {"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/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)\nimage = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\ninput_array = tf.expand_dims(input_array, 0)\npredictions = model(input_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "Load a pre-trained model for image classification from TensorFlow Hub and use it to classify images."}}


 I am making an image recognition app, and I want to extract features from each image.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "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": {"input": "Image tensor", "output": "Feature vector 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/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\nimage = tf.zeros([1, 224, 224, 3])\nfeatures = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This module uses MobileNet v2 architecture for image feature extraction. It takes an image tensor as input and returns a feature vector tensor."}}


 Analyze whether the descriptions of two movies are similar by generating dense vector representations of the text and comparing them.\n
 Use this API documentation for reference:  {"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"], "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": "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"}}


 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
 Use this API documentation for reference:  {"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_130_224/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/mobilenet_v2_130_224/classification/5'\nmodel = hub.load(model_id)\nimage = tf.random.uniform((1, 224, 224, 3))\nprediction = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load pre-trained image classification models from TensorFlow Hub for various tasks such as object recognition, fine-grained classification, and more."}}