 I have a review about a new sci-fi book, and I want to generate high-dimensional vectors that are useful for classification tasks.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Load text embedding models", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "model_url", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello, world!'])", "performance": {"dataset": "Various", "accuracy": "N/A"}, "description": "Load a pre-trained text embedding model from TensorFlow Hub to convert text into fixed-length vector representations."}}


 I have a picture of a beach party, and I want to know how many people are there in the image.\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/efficientdet/d0/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'\ndetector = hub.load(model_id)", "performance": {"dataset": "COCO", "accuracy": "N/A"}, "description": "Load a pre-trained model from TensorFlow Hub for object detection in images."}}


 For document similarity, I want to convert the following sentence into its vector representation: \"Artificial Intelligence is transforming the world in ways we couldn't have imagined a decade ago.\"\n###Input: Artificial Intelligence is transforming the world in ways we couldn't have imagined a decade ago.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["url: https://tfhub.dev/google/universal-sentence-encoder/4"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "Various", "accuracy": "Not specified"}, "description": "A text preprocessing API that loads the Universal Sentence Encoder model from TensorFlow Hub for generating sentence embeddings."}}


 Our team needs to keep track of various laptops and their usage in the workplace. Detect the laptops in the given images and provide their locations.\n
 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'\ndetector = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image.jpg')\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = detector(image)\n", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "A pre-trained object detection model from TensorFlow Hub that can detect objects in images using the Single Shot MultiBox Detector (SSD) with MobileNet V2."}}


 Classify a short clip of people playing football to understand the actions and behaviors of the players.\n
 Use this API documentation for reference:  {"domain": "Video classification", "framework": "TensorFlow Hub", "functionality": "Video classification model", "api_name": "Video_classification_model_id", "api_call": "hub.load('https://tfhub.dev/google/Video_classification_model_id/1')", "api_arguments": [], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "", "performance": {"dataset": "Kinetics-400", "accuracy": ""}, "description": "A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content."}}


 We have several articles to analyze. We need to find a way to compare 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 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."}}


 I have a collection of customer reviews about various products, and I want to find out which reviews are the most similar.\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')\nembeddings = embed(['Hello world!'])\nprint(embeddings)", "performance": {"dataset": "various text classification tasks", "accuracy": "state-of-the-art"}, "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, clustering, and text classification. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs."}}


 I have some animal photos, I want to train a tool will tell me what animal it is.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "94.1%"}, "description": "MobileNetV2 is a family of neural network architectures for efficient on-device image classification and related tasks, originally published by Mark Sandler, et al. This model is pre-trained on the ImageNet dataset and can be used for various image classification tasks."}}


 Show me an example where I can cluster similar movie plots.\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"], "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 0.78"}, "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."}}


 Assist me in identifying food entities in grocery store images.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "mobilenet_v2_100_224", "api_call": "tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')])", "api_arguments": {"url": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4", "output_shape": "[1001]"}, "python_environment_requirements": {"tensorflow": ">=2.0", "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/tf2-preview/mobilenet_v2/classification/4', output_shape=[1001])\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)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNet V2 architecture with 1001 output classes available on TensorFlow Hub."}}


 Based on the text that my customers give me in different languages, create a vector so I can do customer sentiment 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!'])", "performance": {"dataset": "STS", "accuracy": "0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for various natural language processing tasks."}}


 I am building a mobile app that can identify different breeds of dogs, and I need fast feature extraction from images to recognize the breeds.\n
 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": {"image_input": "A 4D tensor with shape [batch_size, height, width, 3]", "output": "A 2D tensor with shape [batch_size, num_features]"}, "python_environment_requirements": {"tensorflow": ">= 2.0", "tensorflow_hub": ">= 0.8"}, "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# Load an example image and preprocess it\nimage = tf.keras.preprocessing.image.load_img('example_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# Extract the features\nfeatures = model.predict(input_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained model that takes a 224x224 RGB image as input and outputs a 1280-dimensional feature vector. Useful for transfer learning and fine-tuning on new image classification tasks."}}


 Compare the similarity between two sentences and provide a numerical score.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Converts text to embeddings", "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 want to analyze customer reviews for sentiment analysis. Provide a method to transform the reviews into fixed-size vectors.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"url": "https://tfhub.dev/google/universal-sentence-encoder/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "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!'])\nprint(embeddings)", "performance": {"dataset": "Various", "accuracy": "Not specified"}, "description": "A pre-trained text embedding model that converts sentences into fixed-size vectors, suitable for various natural language processing tasks."}}


 I need to analyze a group of images and find out which ones are similar. First, I require extracting features of each image for comparison.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extracts feature vector from images", "api_name": "imagenet-mobilenet_v2_140_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[140]"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub\nimport tensorflow as tf\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4', input_shape=[224, 224, 3], output_shape=[140])\n])\nimage = tf.keras.Input(shape=(224, 224, 3))\nfeatures = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "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 machine learning tasks."}}


 In the city, there are many parking lots. We want to count the number of cars parked in a parking lot using a security camera. Can an API help us to do this?\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "ssd_mobilenet_v2_fpnlite_640x640", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_640x640/1')", "api_arguments": {"input_tensor": "4D image tensor", "output_tensor": "list of tensors representing bounding boxes, classes, scores, and number of detections"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\ndetector = hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_640x640/1')\nimage_tensor = tf.convert_to_tensor(image, dtype=tf.uint8)\ninput_tensor = tf.expand_dims(image_tensor, axis=0)\noutput_dict = detector(input_tensor)", "performance": {"dataset": "COCO", "accuracy": "mAP of 0.32"}, "description": "A pre-trained object detection model that detects objects in images using the Single Shot MultiBox Detector (SSD) architecture with MobileNet V2 FPN-Lite feature extractor."}}


 I am going in the forest to observe wildlife, and I want to take a photo of an animal and recognize its species.\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.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nmodel = hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the provided model_id."}}


 I am trying to build a recommendation system that can match similar customer reviews. Convert the given text into a high-dimensional vector that I can use.\n###Input: \"This product is absolutely amazing! I am extremely happy with my purchase and would highly recommend it to others.\"\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "text_input", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'Embedding with Universal Sentence Encoder'])", "performance": {"dataset": "STS benchmark", "accuracy": "0.77"}, "description": "A pre-trained model that converts text into high-dimensional vectors for downstream tasks such as sentiment analysis, translation, and classification."}}


 I would like to analyze the sentiments of some sentences by creating an embedding representation of each sentence.\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": "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."}}


 Our customer support chat service needs to rank responses by their relevance to solve users' issues. So, we need to get embeddings for a list of response options.\n
 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_input", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient > 0.8"}, "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."}}


 Let's say my client wants to use an app to identify different dog breeds by uploading a picture. The app should inform them which breed they just uploaded.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained model for image classification", "api_name": "mobilenet_v2_100_224", "api_call": "hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2')", "api_arguments": {"handle": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2", "input_shape": "(224, 224, 3)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.5.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2', input_shape=(224, 224, 3))\n# Load an image and preprocess it\nimage = tf.keras.preprocessing.image.load_img('path/to/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# Decode predictions\ndecoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy())\nprint(decoded_predictions)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture, with 224x224 input size, and 100% depth multiplier. The model is trained on the ImageNet dataset and can be used for various image classification tasks."}}


 Take a sample sentence and encode it into high-dimensional vector using available API.\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."}}


 Analyze the news and figure out the topics of the articles.\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"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "embeddings = embed(['Hello world', 'Embedding with TensorFlow Hub'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "A pre-trained model that converts text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 I want to build an application that can distinguish different dog breeds from photos. Help me extract features from a set of dog images.\n
 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": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "trainable": "False"}, "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                    trainable=False),\n    tf.keras.layers.Dense(5, activation='softmax')\n])\nmodel.compile(optimizer='adam',\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset, suitable for extracting features from images."}}


 Please create a database of images and their corresponding features using a pre-trained model.\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": "image_tensor", "output": "feature_vector"}, "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])\nimage_tensor = tf.random.uniform(shape=[1, 224, 224, 3])\nfeature_vector = model(image_tensor)\nprint(feature_vector.shape)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API generates a feature vector for an input image using the MobileNetV2 model trained on the ImageNet dataset."}}


 Implement an image classifier to categorize the type of clothing items in a fashion eCommerce dataset.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Load and use a pre-trained image classification model", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"model_url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_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_100_224/classification/4')\ndef classify_image(image_path):\n  image = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))\n  image_array = tf.keras.preprocessing.image.img_to_array(image)\n  image_array = tf.expand_dims(image_array, 0)\n  predictions = model(image_array)\n  return predictions.numpy()", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture with 100% depth multiplier, trained on ImageNet dataset."}}


 Please suggest an API to quickly and easily classify animals in wildlife pictures using a pre-trained model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading and using pre-trained models for image classification", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5'\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_array = tf.expand_dims(image_array, 0)\npredictions = model(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "94.1%"}, "description": "This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images."}}


 Calculate the feature vector of some images of different fruits.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extracts feature vector 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"}, "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_100_224/feature_vector/4')\nimage_tensor = tf.zeros([1, 224, 224, 3])\nfeature_vector = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "This API extracts a feature vector from images using the MobileNetV2 model trained on the ImageNet dataset. It is useful for transfer learning and other tasks that require image embeddings."}}


 I need to classify common items found in a household. How do I do this using a pre-trained model?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "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_tensor = tf.image.resize(image, (224, 224))\npredictions = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 Accuracy: 71.0%, Top-5 Accuracy: 90.1%"}, "description": "A pre-trained image classification model using TensorFlow Hub that can classify images into 1000 different categories. The model is based on the MobileNetV2 architecture and has been trained on the ImageNet dataset."}}


 I'm organizing an animal-based flashcard game for kids, sort these images by animal content using a pre-trained model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "mobilenet_v2", "api_call": "hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": {"handle": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4", "output_shape": "[1001]"}, "python_environment_requirements": {"tensorflow": "2.0", "tensorflow_hub": "0.7"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4', output_shape=[1001])\n# Load an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\nimage = tf.expand_dims(image, 0)\n# Make predictions\npredictions = model(image)\n# Decode predictions\ndecoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=5)\nfor i, (imagenet_id, label, score) in enumerate(decoded_predictions[0]):\n    print('{}. {}: {:.2f}%'.format(i + 1, label, score * 100))", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "MobileNetV2 is a lightweight deep learning model for image classification. It is pre-trained on the ImageNet dataset and can be loaded using TensorFlow Hub."}}


 We need to develop an app to quickly identify plants. Can you provide a pre-trained model to classify plant images?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification 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": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4", "trainable": "False"}, "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/classification/4')\n])\nimage = tf.keras.utils.get_file('image.jpg','https://example.com/image.jpg')\nimg = tf.keras.preprocessing.image.load_img(image, target_size=(224, 224))\nimg_array = tf.keras.preprocessing.image.img_to_array(img)\nimg_array = tf.expand_dims(img_array, 0)\npredictions = model.predict(img_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset."}}


 Design a mobile application which requires a custom model to classify 10 different dog breeds based on their photos.\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": "[batch_size, 224, 224, 3]", "output_shape": "[batch_size, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "model = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    input_shape=(224,224,3), trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture with 100% depth and 224x224 input size. The model is trained on the ImageNet dataset and can be used for feature extraction, fine-tuning, or as a component in a larger model."}}


 We need to identify bird species from some bird images we collected.\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/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(299, 299))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_tensor = tf.expand_dims(image_array, 0)\npredictions = model(image_tensor)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "This API allows users to perform image classification using pre-trained models from TensorFlow Hub. Users can load a model using its model_id and then use it to classify images."}}


 Recommend a neural network layer to obtain a feature vector from a 224x224x3 image for the purpose of similarity search.\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": ">=0.6.0"}, "example_code": "model = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    input_shape=(224, 224, 3),\n                    trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A Keras layer that wraps a pre-trained MobileNetV2 model for feature extraction. It takes an input image of size 224x224x3 and outputs a 1280-dimensional feature vector."}}


 We want to find semantically similar sentences in our customer reviews. Obtain a high-dimensional vector of each review sentence.\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"], "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"}}


 Let's say we have a dataset of images and we want to compress and store them. Design a method for feature extraction and compression.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract feature vectors from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]", "trainable": "False"}, "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),\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": "71.9%"}, "description": "This module uses the MobileNet v2 architecture to extract feature vectors from images. It has been pre-trained on the ImageNet dataset and can be fine-tuned for various classification tasks."}}


 I took a picture of a plant in my garden, and I want to identify it using an image classification model.\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/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# Example image\nimage = tf.keras.utils.get_file('image.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg')\n# Preprocess image\nimage = tf.keras.preprocessing.image.load_img(image, target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\nimage_batch = tf.expand_dims(image, 0)\n# Make predictions\npredictions = loaded_model(image_batch)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture, trained on ImageNet dataset."}}


 I'm building a mobile app that classifies images taken by users. The model should be lightweight and fast.\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/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)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the specified model_id."}}


 I have a collection of images from an automobile trade show and I want to identify the different vehicle models present in those images.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Load 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": {"model_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')\ninput_image = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(input_image) / 255.0\ninput_tensor = tf.expand_dims(input_array, 0)\npredictions = model(input_tensor)\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 accuracy: 75.6%, Top-5 accuracy: 92.8%"}, "description": "A pre-trained MobileNetV2 model for image classification on the ImageNet dataset. The model is designed to be small and efficient while maintaining high accuracy. It can be used for various image classification tasks."}}


 Recommend a solution to classify diseases from the images of plant leaves.\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."}}


 We need to extract features from a large set of images to use as input for an image classifier.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extracting 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/5')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[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/5',\n                   input_shape=(224, 224, 3),\n                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=5)", "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 various image classification tasks by adding a classification head on top of the feature vector."}}


 In music recommendation systems, we need to represent the songs as feature vectors. How can we do this?\n
 Use this API documentation for reference:  {"domain": "Audio embedding", "framework": "TensorFlow Hub", "functionality": "Converts audio into embeddings", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/yamnet/1')", "api_arguments": ["audio"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/yamnet/1'\nmodel = hub.load(model_id)\nembeddings = model(audio)", "performance": {"dataset": "AudioSet", "accuracy": "Not provided"}, "description": "YAMNet is a deep net that predicts 521 audio event classes based on the AudioSet dataset. It employs the Mobilenet_v1 depthwise-separable convolution architecture."}}


 I have a list of user reviews on my shopping website, and I want to cluster them into different categories.\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 input"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.80"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Somebody sent me a photo of a car brand's logo, and I need to identify the brand.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "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": ["URL"], "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/mobilenet_v2_130_224/classification/4')\n])\nresult = model.predict(image_batch)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained model for image classification using MobileNetV2 architecture on the ImageNet dataset."}}


 I want to build an image-based recommendation system. Please suggest a way to classify images more efficiently.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "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": "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 = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'),\n])\n", "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."}}


 Create a recommendation system that recommends similar products using product descriptions.\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!'])\nprint(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."}}


 Analyze public opinions on a new coffee flavor and find out if they are related to each other.\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!'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "0.80"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 I am an app developer and I need a lightweight image classification model to classify images into different categories. How can I do that?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using a pre-trained model", "api_name": "mobilenet_v2", "api_call": "hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": {"model_url": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4", "input_shape": "IMAGE_SHAPE+(3,)"}, "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nIMAGE_SHAPE = (224, 224)\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4', input_shape=IMAGE_SHAPE+(3,))\n])\nimage = tf.keras.preprocessing.image.load_img('image.jpg', target_size=IMAGE_SHAPE)\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\nresult = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices."}}


 I am not good at recognizing sports, sometimes I make mistakes. Therefore, I need a tool to check the answer for me.\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/mobilenet_v2_130_224/classification/5')", "api_arguments": [{"model_id": "string (URL or local path to the model)"}], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5'\nmodel = hub.load(model_id)\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\n# Classify the image\npredictions = model(image_array)\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\nprint('Predicted class:', predicted_class)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained model for image classification using TensorFlow Hub."}}


 Can you please make model to classify type of car\\vehicle from an image?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "imagenet_mobilenet_v1_050_160_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v1_050_160/classification/4')", "api_arguments": "URL", "python_environment_requirements": "tensorflow, tensorflow-hub", "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Loads a pre-trained MobileNet V1 model for image classification from TensorFlow Hub"}}


 Analyze online customer reviews and their sentiment by getting an embedding of each review.\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!', 'Greetings, humans!'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "A pre-trained model for embedding text into high-dimensional vectors that can be used in various natural language processing tasks."}}


 I want a system that can find the similarity between different movie reviews.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text using a pre-trained model", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["text_input"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STS benchmark", "accuracy": "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 company wants to build a recyclable materials recognition system. We plan to reuse some pre-trained models for transfer learning.\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": "[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',\n                   input_shape=[224, 224, 3],\n                   output_shape=[1280])\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": "A pre-trained image feature vector model based on MobileNetV2 architecture, which can be used for image classification or as a feature extractor for transfer learning."}}


 We want to help our users analyze their posture while performing exercises. Help us detect human poses in images to do that.\n
 Use this API documentation for reference:  {"domain": "Image pose detection", "framework": "TensorFlow Hub", "functionality": "Detect human poses in images", "api_name": "movenet", "api_call": "hub.load('https://tfhub.dev/google/movenet/singlepose/lightning/4')", "api_arguments": {"input_image": "A single image or batch of images with shape [batch_size, height, width, 3]"}, "python_environment_requirements": {"tensorflow": ">=2.5.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\npose = hub.load('https://tfhub.dev/google/movenet/singlepose/lightning/4')\ninput_image = tf.zeros([1, 192, 192, 3])\noutput = pose(input_image)", "performance": {"dataset": "COCO", "accuracy": "0.720"}, "description": "MoveNet is a model that detects human poses in images. It is trained on the COCO dataset and available on TensorFlow Hub."}}


 We need a way to classify and identify various objects in images, suggest a pre-trained model for that.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification model", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": {"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 = 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/your/image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, 0)\npredictions = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained image classification model using MobileNetV2 architecture trained on ImageNet dataset."}}


 Let's take a picture from our hike yesterday, classify the primary plants or flowers in it.\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.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": "URL of the pre-trained model", "python_environment_requirements": "TensorFlow 2.x, TensorFlow Hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n# Load an image and preprocess it\ndef load_image(file_path):\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\n    return img_array\n# Predict the class of the image\nimg_array = load_image('path/to/your/image.jpg')\npredictions = model(img_array)\n# Get the class with the highest probability\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories."}}


 We are going to develop security cameras to monitor our client's warehouse for unauthorized access.\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'\nloaded_model = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg')\nimage_np = np.array(image)\ninput_tensor = tf.convert_to_tensor(image_np)\ninput_tensor = input_tensor[tf.newaxis, ...]\noutput_dict = loaded_model(input_tensor)\nboxes = output_dict['detection_boxes'][0].numpy()\nscores = output_dict['detection_scores'][0].numpy()\nclasses = output_dict['detection_classes'][0].numpy().astype(np.int32)", "performance": {"dataset": "COCO", "accuracy": "0.320"}, "description": "This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores."}}


 I have a large collection of restaurant reviews in English, and I want to cluster them based on their content.\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": "0.803"}, "description": "A model for encoding variable-length English text into fixed-length high-dimensional vectors. It is useful for applications such as semantic similarity, clustering, or text classification."}}


 Detect the fashion items in given images and create a model for fashion item feature extraction using pretrained image Feature Vector.\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": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture for image classification tasks."}}


 Determine which type of fruit is in the photograph by analyzing and classifying the image.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"url": "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 as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\nimage = tf.zeros([1, 224, 224, 3])  # A batch of images with shape [batch_size, height, width, 3].\nlogits = model(image)  # Logits with shape [batch_size, num_classes].", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "MobileNetV2 model for image classification, pre-trained on ImageNet and optimized for TensorFlow Hub."}}


 We want to build an image recognition system to classify paintings by artists. Please show us how to use a pretrained model for feature extraction from the paintings.\n
 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": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4'),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\n              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=5)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset."}}


 We want to generate embeddings for a list of movie descriptions so we can later measure the similarity between these movies.\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.KerasLayer('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "Text or list of texts", "output": "Array of embeddings"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.KerasLayer('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'Embedding is fun!'])", "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."}}


 Tell me what animal I have in my picture. How can I achieve this using a pre-trained TensorFlow model?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4", "trainable": "Optional, default is False"}, "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(\n        handle='https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4',\n        input_shape=(224, 224, 3),\n        trainable=False\n    )\n])\nimage = tf.keras.preprocessing.image.load_img('path_to_your_image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "80.3%"}, "description": "A pre-trained image classification model using the MobileNetV2 architecture, trained on the ImageNet dataset. It is designed for efficient on-device performance with reduced computational and memory requirements."}}


 We have a long list of customer reviews for our products. We need to determine which ones are similar so we can improve specific products.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Convert text into embeddings", "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": "85.1% Pearson correlation"}, "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 have a folder containing images of animals, and I would like to automatically label these images with the name of the animal they contain.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification 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 of the pre-trained model", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n])\npredictions = model.predict(images)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset"}}


 I have an online marketplace where people sell secondhand items. I would like to categorize the photos of the products people upload.\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."}}


 Provide me with the ability to identify objects present in a given image, for improving the accessibility of our application.\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."}}


 I have a collection of paintings and I want to extract their main features to use for indexing among different categories.\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": "[batch_size, 224, 224, 3]", "output_shape": "[batch_size, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224, 224, 3),\n                   output_shape=(1280,),\n                   trainable=False)\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API provides a feature vector for images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have shape (224, 224, 3), and the output feature vector has 1280 dimensions. The model is available on TensorFlow Hub and can be used for transfer learning applications."}}


 Calculate the similarity between two paragraphs of text using embeddings.\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."}}


 I'd like to analyze bird songs to identify their species. I need an audio model that can help me extract features from the recordings.\n
 Use this API documentation for reference:  {"domain": "Audio embedding", "framework": "TensorFlow Hub", "functionality": "Audio embedding generation", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/yamnet/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/yamnet/1'\nmodel = hub.load(model_id)\nembeddings = model(samples, sample_rate)", "performance": {"dataset": "YAMNet", "accuracy": "Not specified"}, "description": "This API generates audio embeddings using a pre-trained model from TensorFlow Hub. The model can be loaded with a specified model_id, and embeddings can be extracted for input audio samples."}}


 Let's create a dog breed classifier and use the pretrained ImageNet MobileNet V2 model to extract dog breed 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": ["URL"], "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/mobilenet_v2_100_224/feature_vector/4',\n                    input_shape=(224, 224, 3),\n                    trainable=False),\n    tf.keras.layers.Dense(5, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNet V2 architecture for extracting features from images. The model is trained on ImageNet dataset and has an accuracy of 71.9%."}}


 Recommend products similar to the given product description. Use a pre-built model to find the semantic similarity with other product descriptions.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Text embedding using a pre-trained model", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello, world!', 'Embed text with Universal Sentence Encoder'])", "performance": {"dataset": "various", "accuracy": "N/A"}, "description": "A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks."}}


 I need to identify the type of food in each picture I have in my album, so I need tensor values of the pretrained model \"imagenet-inception_v3-feature_vector\".\n###Input: {\"url\": \"https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\"}\n
 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/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": "Not specified"}, "description": "Load a pre-trained model from TensorFlow Hub for image classification using the Inception V3 architecture."}}


 I'm building a chatbot and I need to analyze the similarity between pairs of 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": "List of sentences"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world', 'I am learning how to use the Universal Sentence Encoder'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 I captured a photo of an unknown animal, and I want to know the animal's classification.\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."}}


 We need a way to detect objects on the road like stop signs, other vehicles, and pedestrians in our autonomous vehicle project. Suggest an API that can help us do that.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Identify and locate objects in images", "api_name": "ssd_mobilenet_v2", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": {"input": "Tensor of shape [batch_size, height, width, 3]", "output": "Dictionaries with keys 'detection_boxes', 'detection_classes', 'detection_scores'"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\ndetector = hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')\nimage_tensor = tf.image.decode_image(tf.io.read_file('path/to/image.jpg'))\nimage_tensor = tf.expand_dims(image_tensor, 0)\nresult = detector(image_tensor)\nprint(result)", "performance": {"dataset": "COCO", "accuracy": "mAP of 0.22"}, "description": "A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects in images."}}


 Provide a way to set up an image classification system that automates identification of photographs.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained image classification model", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": ["URL of the pre-trained model"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "Loads a pre-trained MobileNetV2 model for image classification trained on the ImageNet dataset with an input size of 224x224 pixels and a depth multiplier of 1.0."}}


 I am making an AI chatbot that responds to user messages. I need to create sentence embeddings efficiently.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["url: https://tfhub.dev/google/universal-sentence-encoder/4"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "Various", "accuracy": "Not specified"}, "description": "A text preprocessing API that loads the Universal Sentence Encoder model from TensorFlow Hub for generating sentence embeddings."}}


 Find a way to showcase the usage of a model to detect objects in a picture taken in a factory to count how many different pieces of equipment are there.\n
 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'\ndetector = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image.jpg')\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = detector(image)\n", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "A pre-trained object detection model from TensorFlow Hub that can detect objects in images using the Single Shot MultiBox Detector (SSD) with MobileNet V2."}}


 Convert an image to a feature vector that can be used in content-based image retrieval system.\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."}}


 We're working on a travel blogging platform, and we need a way to extract the features of images that users upload.\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": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNetV2, designed for efficient feature extraction from images."}}


 Use the embeddings to find cluster seeds for a given set of job descriptions.\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 as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "various", "accuracy": "not specified"}, "description": "A pre-trained model that encodes text into 512-dimensional embeddings using a deep averaging network (DAN). Useful for a variety of natural language processing tasks."}}


 The manager wants to build a product recommender system based on the types of items in customers\u2019 photos. Determine what each item is in the image.\n
 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 find the best matching property images from an image database for a real estate application.\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', input_shape=(224, 224, 3), trainable=False)\n])\nimage = tf.random.normal([1, 224, 224, 3])\nfeatures = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. The model is designed to extract features from images and can be used for various tasks such as image classification, object detection, and image similarity."}}


 I have a dataset of photos, and I need to classify all of those photos into different categories, so I'm thinking of using a pre-trained model.\n
 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."}}


 Find objects in city surveillance camera images and get the objects with their corresponding detection scores.\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": [{"name": "model_id", "type": "string", "description": "The ID of the model to load from TensorFlow Hub"}], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'\ndetector = hub.load(model_id)\n# Load an image, preprocess it, and run the detector\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg')\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.image.resize(input_image, (300, 300))\ninput_image = tf.expand_dims(input_image, 0)\n# Run the detector\noutput = detector(input_image)\n# Process the output\nfor i in range(output['num_detections']):\n    if output['detection_scores'][i] > 0.5:\n        print('Detected object:', output['detection_class_entities'][i], 'with confidence:', output['detection_scores'][i])", "performance": {"dataset": "COCO", "accuracy": "mAP@0.5IOU"}, "description": "A pre-trained object detection model that can detect objects in images using TensorFlow Hub"}}


 Compare news articles for similarity and rank them.\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": "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."}}


 We have some random images and we would like to classify them into different categories based on their context.\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.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.x", "tensorflow_hub": "latest"}, "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", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture with 100% depth and 224x224 input size, trained on ImageNet dataset."}}