 Generate an API that performs image classification using a small model with low computational requirements.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'HarDNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'PingoLH/Pytorch-HarDNet\', model=\'hardnet68\', pretrained=True)", \'api_arguments\': [{\'name\': \'hardnet68\', \'type\': \'str\', \'description\': \'HarDNet-68 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'PingoLH/Pytorch-HarDNet\', \'hardnet68\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'hardnet68\': {\'Top-1 error\': 23.52, \'Top-5 error\': 6.99}}}, \'description\': \'Harmonic DenseNet (HarDNet) is a low memory trafficCNN model, which is fast and efficient. The basic concept is to minimize both computational cost and memory access cost at the same time, such that the HarDNet models are 35% faster than ResNet running on GPU comparing to models with the same accuracy (except the two DS models that were designed for comparing with MobileNet).\'}', metadata={})]


 I need an efficient AI-based classifier to identify products on grocery store shelves. Suggest an appropriate API to implement this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I want to perform image classification for optimizing the storage space of a database. Provide an API that enables this while maintaining accuracy.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I am a content writer for Marvel Studios and I am trying to categorize certain images of the characters based on their similarity. Recommend an API that can classify an image of a Marvel character.\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 digital artist needs an API that can recognize and classify images containing multiple objects. Which API would you suggest?\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={})]


 Suggest an API for a wildlife conservation organization that could help them identify animals from images captured by their research cameras.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 What would be a suitable API for an application that classifies images of autonomous driving from different devices and should be efficient in terms of size?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I am a developer at Audible and I am looking for an API that can convert text to speech, find something suitable.\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={})]


 You are tasked to parse images in a storage platform to classify a set of new products. Suggest me an API that can help you do this classification task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 I am building an app to identify poisonous and non-poisonous mushrooms by taking a picture of it. Suggest an API to help me classify the pictures taken.\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={})]


 Can you provide me an API for classifying a video content based on the actions performed in it?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video Classification\', \'framework\': \'PyTorchVideo\', \'functionality\': \'X3D Networks\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorchvideo\', model=\'x3d_s\', pretrained=True)", \'api_arguments\': {\'repository\': \'facebookresearch/pytorchvideo\', \'model\': \'x3d_s\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'pytorchvideo\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/pytorchvideo\', \'x3d_s\', pretrained=True)", "device = \'cpu\'", \'model = model.eval()\', \'model = model.to(device)\'], \'performance\': {\'dataset\': \'Kinetics 400\', \'accuracy\': {\'top1\': 73.33, \'top5\': 91.27}, \'flops\': 2.96, \'params\': 3.79}, \'description\': "X3D model architectures are based on the paper \'X3D: Expanding Architectures for Efficient Video Recognition\' by Christoph Feichtenhofer. They are pretrained on the Kinetics 400 dataset. This model is capable of classifying video clips into different action categories. It is provided by the FAIR PyTorchVideo library."}', metadata={})]


 A startup called \"DriveMe\" is building a vehicular safety app and wants to detect traffic objects, segment drivable areas, and detect lanes in real-time. Suggest an API to help them achieve their goal.\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 API which detects voice activity in an audio file and share the code to load it.\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={})]


 Help me identify various objects in an image. Suggest an API for performing image classification.\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 marketing company needs an API to classify images into animals and assign them different categories. Which API would you recommend them?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'GPUNet Networks\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_gpunet\', pretrained=True, model_type=model_type, model_math=precision)", \'api_arguments\': {\'repository\': \'NVIDIA/DeepLearningExamples:torchhub\', \'model\': \'nvidia_gpunet\', \'pretrained\': \'True\', \'model_type\': \'GPUNet-0\', \'model_math\': \'fp32\'}, \'python_environment_requirements\': [\'torch\', \'validators\', \'matplotlib\', \'timm==0.5.4\'], \'example_code\': [\'import torch\', "model_type = \'GPUNet-0\'", "precision = \'fp32\'", "gpunet = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_gpunet\', pretrained=True, model_type=model_type, model_math=precision)", "device = torch.device(\'cuda\') if torch.cuda.is_available() else torch.device(\'cpu\')", \'gpunet.to(device)\', \'gpunet.eval()\'], \'performance\': {\'dataset\': \'IMAGENET\', \'description\': \'GPUNet demonstrates state-of-the-art inference performance up to 2x faster than EfficientNet-X and FBNet-V3.\'}, \'description\': \'GPUNet is a family of Convolutional Neural Networks designed by NVIDIA using novel Neural Architecture Search (NAS) methods. They are optimized for NVIDIA GPU and TensorRT performance. GPUNet models are pretrained on the IMAGENET dataset and are capable of classifying images into different categories. The models are provided by the NVIDIA Deep Learning Examples library.\'}', metadata={})]


 Recommend an API for a mobile app that can identify fruits from images taken by the users.\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 city is planning to survey the land for urban development. Provide me with an API that can identify buildings and roads from an aerial photo.\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 model for classifying animals in images taken by wildlife cameras. Suggest me an API for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Semi-supervised and semi-weakly supervised ImageNet Models\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/semi-supervised-ImageNet1K-models\', model=\'resnet18_swsl\', pretrained=True)", \'api_arguments\': {\'repository\': \'facebookresearch/semi-supervised-ImageNet1K-models\', \'model\': \'resnet18_swsl\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/semi-supervised-ImageNet1K-models\', \'resnet18_swsl\', pretrained=True)", \'model.eval()\'], \'performance\': {\'description\': \'Semi-supervised and semi-weakly supervised ImageNet models achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture.\'}, \'description\': "Semi-supervised and semi-weakly supervised ImageNet Models are introduced in the \'Billion scale semi-supervised learning for image classification\' paper. These models are pretrained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset. They are capable of classifying images into different categories and are provided by the Facebook Research library."}', metadata={})]


 The company is creating a neural network model that can run efficiently on different hardware platforms. Tell me an API that specializes CNNs for different hardware.\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={})]


 Farlando Corp has an application that runs on their customers' GPUs, and they want a neural network that is optimized on GPU performance. Recommend an API that they can use for image classification.\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 model for image classification with good accuracy. Provide me with an API that uses LIF neurons.\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={})]


 As a market research analyst, I want to find a tool to classify different product types using their images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text-to-Speech\', \'framework\': \'PyTorch\', \'functionality\': \'Speech Synthesis\', \'api_name\': \'WaveGlow\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_waveglow\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'NVIDIA/DeepLearningExamples:torchhub\', \'model\': \'nvidia_waveglow\', \'model_math\': \'fp32\'}, \'python_environment_requirements\': [\'numpy\', \'scipy\', \'librosa\', \'unidecode\', \'inflect\', \'libsndfile1\'], \'example_code\': {\'load_waveglow_model\': "waveglow = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_waveglow\', model_math=\'fp32\')", \'prepare_waveglow_model\': [\'waveglow = waveglow.remove_weightnorm(waveglow)\', "waveglow = waveglow.to(\'cuda\')", \'waveglow.eval()\'], \'load_tacotron2_model\': "tacotron2 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_tacotron2\', model_math=\'fp32\')", \'prepare_tacotron2_model\': ["tacotron2 = tacotron2.to(\'cuda\')", \'tacotron2.eval()\'], \'synthesize_speech\': [\'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\'], \'save_audio\': \'write("audio.wav", rate, audio_numpy)\', \'play_audio\': \'Audio(audio_numpy, rate=rate)\'}, \'performance\': {\'dataset\': \'LJ Speech\', \'accuracy\': None}, \'description\': \'The Tacotron 2 and WaveGlow model form a text-to-speech system that enables users to synthesize natural-sounding speech from raw transcripts without any additional prosody information. The Tacotron 2 model produces mel spectrograms from input text using encoder-decoder architecture. WaveGlow is a flow-based model that consumes the mel spectrograms to generate speech.\'}', metadata={})]


 A media company that works with image recognition is trying to identify an object in an image. Recommend an API that specializes in image recognition.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 Inform me of an API that can help identify famous landmarks from images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I am working on an image classification project where accuracy is important, and I need a pretrained model that has a lower error rate when classifying images. What model might work for me?\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={})]


 The New York Times wants to classify some information about Jim Henson. Recommend an API to analyze and classify the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 Recommend a pretrained API that classifies animals from an image given the photo of the animal.\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 have a picture of my dog and I want to classify its breed. Provide me an API to do this.\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={})]


 A developer at Pinterest wants to automatically categorize uploaded images based on their content. Provide an API suggestion that can help with this task.\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 startup is working on a computer vision application supporting autonomous drones. Can you provide an API that can compute the relative depth of an object in a given image?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 Imagine you are trying to build podcast transcription for people who are impaired. Get an API to transcribe a sample podcast from Spotify.\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={})]


 A tourist is planning to take a picture of a beautiful scene but wants to separate the people from the background. Recommend an API to help do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'Inception_v3\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'inception_v3\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'1.9.0\', \'torchvision\': \'0.10.0\'}, \'example_code\': {\'import_libraries\': \'import torch\', \'load_model\': "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'inception_v3\', pretrained=True)", \'model_evaluation\': \'model.eval()\'}, \'performance\': {\'dataset\': \'imagenet\', \'accuracy\': {\'top-1_error\': 22.55, \'top-5_error\': 6.44}}, \'description\': \'Inception v3, also called GoogleNetv3, is a famous Convolutional Neural Network trained on the ImageNet dataset from 2015. It is based on the exploration of ways to scale up networks to utilize the added computation as efficiently as possible by using suitably factorized convolutions and aggressive regularization. The model achieves a top-1 error of 22.55% and a top-5 error of 6.44% on the ImageNet dataset.\'}', metadata={})]


 I took a photo and I want to detect all the objects in the image. Provide me with an API to 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={})]


 Find an API that can generate new images of various clothing styles in 64x64 resolution using Generative Adversarial Networks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Generative Adversarial Networks\', \'api_name\': \'DCGAN\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorch_GAN_zoo:hub\', model=\'DCGAN\', pretrained=True, useGPU=use_gpu)", \'api_arguments\': {\'pretrained\': \'True\', \'useGPU\': \'use_gpu\'}, \'python_environment_requirements\': \'Python 3\', \'example_code\': {\'import\': [\'import torch\', \'import matplotlib.pyplot as plt\', \'import torchvision\'], \'use_gpu\': \'use_gpu = True if torch.cuda.is_available() else False\', \'load_model\': "model = torch.hub.load(\'facebookresearch/pytorch_GAN_zoo:hub\', \'DCGAN\', pretrained=True, useGPU=use_gpu)", \'build_noise_data\': \'noise, _ = model.buildNoiseData(num_images)\', \'generate_images\': \'with torch.no_grad(): generated_images = model.test(noise)\', \'plot_images\': [\'plt.imshow(torchvision.utils.make_grid(generated_images).permute(1, 2, 0).cpu().numpy())\', \'plt.show()\']}, \'performance\': {\'dataset\': \'FashionGen\', \'accuracy\': \'N/A\'}, \'description\': \'DCGAN is a model designed in 2015 by Radford et. al. in the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. It is a GAN architecture both very simple and efficient for low resolution image generation (up to 64x64).\'}', metadata={})]


 I am trying to classify an image to find its category. Please give me an API that can identify the content of an image.\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={})]


 I would like to convert text to natural sounding speech using Deep Learning. Can you provide me with an API to achieve this?\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={})]


 Design a system to diagnose diseases from X-Ray images. Recommend an appropriate API for classifying diseases in the X-Ray images.\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 smartphone company is developing an app that can classify object from a picture. Provide an API that can achieve this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I want to create an app that recognizes items from pictures taken by users. Can you recommend any machine learning API for this purpose?\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={})]


 Recommend an API that can be used for image classification tasks on a dataset of images.\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={})]


 Find out an API that can identify 102 different types of flowers from an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 Can you recommend an API for image classification which is efficient in terms of computational resources and has decent accuracy?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SqueezeNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'squeezenet1_1\', pretrained=True)", \'api_arguments\': {\'version\': \'v0.10.0\', \'model\': [\'squeezenet1_1\'], \'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\', \'squeezenet1_1\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'squeezenet1_1\': {\'Top-1 error\': 41.81, \'Top-5 error\': 19.38}}}, \'description\': \'SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.\'}', metadata={})]


 A photography service needs a fast algorithm to recognize objects in their images from the ImageNet dataset out of the box. What API should they use?\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={})]


 Can you suggest an API for classifying images in my dataset using a model with spiking neural networks?\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 am trying to recognize objects in an image using a popular image classification model. Which model should I use?\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 want to create an app to recognize objects in images. Which API is suitable for this task?\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={})]


 Air Traffic Control needs an image classifier to identify if an image contains an aircraft or not. Suggest an API that would be suitable for this task.\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 smart fridge wants to identify food items from images taken from its camera. Provide an API to identify the food items.\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 want to count how many people are present in a room using an image. Tell me an API that can do this task.\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={})]


 I am developing a website that can predict the content of an image based on its URL. What API would you recommend with a code example?\n###Input: {\"image_url\": \"https://example.com/image.jpg\"}\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={})]


 A wildlife photographer wants to classify animals in images taken during a safari. Provide me with an API that can help classify these animals.\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 want to use my camera app to identify objects that I point it to. What API would you recommend?\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={})]


 I am building an image classification model and want to achieve a high accuracy. Which API should I use?\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={})]


 A photographer at a film studio wants to find the relative depth from a single image. Recommend an API that can compute relative depth from an input image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Computing relative depth from a single image\', \'api_name\': \'MiDaS\', \'api_call\': "torch.hub.load(repo_or_dir=\'intel-isl/MiDaS\', model=\'DPT_Large\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'intel-isl/MiDaS\', \'model\': \'model_type\'}, \'python_environment_requirements\': \'pip install timm\', \'example_code\': [\'import cv2\', \'import torch\', \'import urllib.request\', \'import matplotlib.pyplot as plt\', "url, filename = (\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\', \'dog.jpg\')", \'urllib.request.urlretrieve(url, filename)\', "model_type = \'DPT_Large\'", "midas = torch.hub.load(\'intel-isl/MiDaS\', \'DPT_Large\')", "device = torch.device(\'cuda\') if torch.cuda.is_available() else torch.device(\'cpu\')", \'midas.to(device)\', \'midas.eval()\', "midas_transforms = torch.hub.load(\'intel-isl/MiDaS\', \'transforms\')", "if model_type == \'DPT_Large\' or model_type == \'DPT_Hybrid\':", \' transform = midas_transforms.dpt_transform\', \'else:\', \' transform = midas_transforms.small_transform\', \'img = cv2.imread(filename)\', \'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\', \'input_batch = transform(img).to(device)\', \'with torch.no_grad():\', \' prediction = midas(input_batch)\', \'prediction = torch.nn.functional.interpolate(\', \' prediction.unsqueeze(1),\', \' size=img.shape[:2],\', " mode=\'bicubic\',", \' align_corners=False,\', \').squeeze()\', \'output = prediction.cpu().numpy()\', \'plt.imshow(output)\', \'plt.show()\'], \'performance\': {\'dataset\': \'10 distinct datasets\', \'accuracy\': \'Multi-objective optimization\'}, \'description\': \'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\'}', metadata={})]


 A bird watching society is developing an app that can identify birds in a picture. Provide a suitable API that can be used for classifying birds from images.\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={})]


 Provide an API recommendation for a call center which wants to convert customer voice calls into text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 Provide me with an API that can tackle city-scape segmentation in autonomous driving application.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I need an API to extract features from a collection of photographs taken at the 2022 Olympics.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Efficient networks by generating more features from cheap operations\', \'api_name\': \'GhostNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/ghostnet\', model=\'ghostnet_1x\', pretrained=True)", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/ghostnet\', \'ghostnet_1x\', pretrained=True)", \'model.eval()\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 acc\': \'73.98\', \'Top-5 acc\': \'91.46\'}}, \'description\': \'The GhostNet architecture is based on an Ghost module structure which generates more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features. Experiments conducted on benchmarks demonstrate the superiority of GhostNet in terms of speed and accuracy tradeoff.\'}', metadata={})]


 An E-commerce manager wants to develop an image classification system for their products. They need a powerful pre-trained model as a starting point. Recommend an API for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Semantic Segmentation\', \'framework\': \'PyTorch\', \'functionality\': \'Neural Machine Translation\', \'api_name\': \'Transformer (NMT)\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/fairseq\')", \'api_arguments\': [\'model_name\', \'tokenizer\', \'bpe\', \'beam\', \'sampling\', \'sampling_topk\'], \'python_environment_requirements\': [\'bitarray\', \'fastBPE\', \'hydra-core\', \'omegaconf\', \'regex\', \'requests\', \'sacremoses\', \'subword_nmt\'], \'example_code\': "import torch\\n\\nen2fr = torch.hub.load(\'pytorch/fairseq\', \'transformer.wmt14.en-fr\', tokenizer=\'moses\', bpe=\'subword_nmt\')\\n\\nen2fr.cuda()\\n\\nfr = en2fr.translate(\'Hello world!\', beam=5)\\nassert fr == \'Bonjour à tous !\'", \'performance\': {\'dataset\': [{\'name\': "WMT\'14", \'accuracy\': \'Not provided\'}, {\'name\': "WMT\'18", \'accuracy\': \'Not provided\'}, {\'name\': "WMT\'19", \'accuracy\': \'Not provided\'}]}, \'description\': "Transformer (NMT) is a powerful sequence-to-sequence modeling architecture that produces state-of-the-art neural machine translation systems. It is based on the paper \'Attention Is All You Need\' and has been improved using techniques such as large-scale semi-supervised training, back-translation, and noisy-channel reranking. It supports English-French and English-German translation as well as round-trip translation for paraphrasing."}', metadata={})]