 I need an API to classify images with known objects. Suggest a suitable model that can do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 A delivery company wants to recognize if a package is damaged during shipment. Propose an API that can classify images into damaged and undamaged packages.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 An image recognition app needs to identify objects from the images it captures. Suggest an API which is optimized for GPUs.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Show me an API that provides easy to use neural networks for classifying different types of wildlife on mobile platforms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Once-for-all (OFA) Networks\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'mit-han-lab/once-for-all\', model=\'ofa_supernet_mbv3_w10\', pretrained=True)", \'api_arguments\': {\'repository\': \'mit-han-lab/once-for-all\', \'model\': \'ofa_supernet_mbv3_w10\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "super_net_name = \'ofa_supernet_mbv3_w10\'", "super_net = torch.hub.load(\'mit-han-lab/once-for-all\', super_net_name, pretrained=True).eval()"], \'performance\': {\'description\': \'OFA networks outperform state-of-the-art NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and CO2 emission.\'}, \'description\': \'Once-for-all (OFA) networks are a family of neural networks designed by MIT Han Lab. They decouple training and search, achieving efficient inference across various edge devices and resource constraints. OFA networks are pretrained on the IMAGENET dataset and are capable of classifying images into different categories.\'}', metadata={})]


 Recommend an API for identifying defective parts in a manufacturing assembly line based on images taken by an inspection system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 Identify an image classification API that can be used to determine if an object is a car, a bike, or a pedestrian.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 I need an API to classify images efficiently without sacrificing too much accuracy. Can you provide me with one?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 To save the environment, a student wants to evaluate how green his schools area is. Tell me an AI API which can classify the images of plants in his environment and tell the name of the plants.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]


 I need an efficient API to classify images on multiple edge devices with different resource constraints. Suggest one for me.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Once-for-all (OFA) Networks\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'mit-han-lab/once-for-all\', model=\'ofa_supernet_mbv3_w10\', pretrained=True)", \'api_arguments\': {\'repository\': \'mit-han-lab/once-for-all\', \'model\': \'ofa_supernet_mbv3_w10\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "super_net_name = \'ofa_supernet_mbv3_w10\'", "super_net = torch.hub.load(\'mit-han-lab/once-for-all\', super_net_name, pretrained=True).eval()"], \'performance\': {\'description\': \'OFA networks outperform state-of-the-art NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and CO2 emission.\'}, \'description\': \'Once-for-all (OFA) networks are a family of neural networks designed by MIT Han Lab. They decouple training and search, achieving efficient inference across various edge devices and resource constraints. OFA networks are pretrained on the IMAGENET dataset and are capable of classifying images into different categories.\'}', metadata={})]


 I want my app to be able to read aloud the text for audiobooks. Can you suggest me an API for converting text to speech?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'Tacotron 2\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_tacotron2\', model_math=\'fp16\')", \'api_arguments\': {\'model_math\': \'fp16\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': [\'import torch\', "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp16\')", "tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\', "text = \'Hello world, I missed you so much.\'", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tts_utils\')", \'sequences, lengths = utils.prepare_input_sequence([text])\', \'with torch.no_grad():\', \' mel, _, _ = tacotron2.infer(sequences, lengths)\', \' audio = waveglow.infer(mel)\', \'audio_numpy = audio[0].data.cpu().numpy()\', \'rate = 22050\'], \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': \'Not specified\'}, \'description\': \'The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 An app wants to identify dog breeds from images taken by users. Recommend an API that can classify the dog breed given a photo of a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Single Shot MultiBox Detector\', \'api_name\': \'SSD\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_ssd\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'numpy\', \'scipy\', \'scikit-image\', \'matplotlib\'], \'example_code\': "import torch\\n\\nssd_model = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_ssd_processing_utils\')\\n\\nssd_model.to(\'cuda\')\\nssd_model.eval()\\n\\ninputs = [utils.prepare_input(uri) for uri in uris]\\ntensor = utils.prepare_tensor(inputs)\\n\\nwith torch.no_grad():\\n detections_batch = ssd_model(tensor)\\n\\nresults_per_input = utils.decode_results(detections_batch)\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]", \'performance\': {\'dataset\': {\'name\': \'COCO\', \'accuracy\': \'Not provided\'}}, \'description\': "The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \'SSD: Single Shot MultiBox Detector\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency."}', metadata={})]