 I want to build an image classifier to boost the accuracy of the Vanilla Resnet-50 model on ImageNet data without using any data augmentation tricks. What 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={})]


 Create a 3D reconstruction of a scene from only one image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video Classification\', \'framework\': \'PyTorch\', \'functionality\': \'3D ResNet\', \'api_name\': \'slow_r50\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorchvideo\', model=\'slow_r50\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'json\', \'urllib\', \'pytorchvideo\', \'torchvision\', \'torchaudio\', \'torchtext\', \'torcharrow\', \'TorchData\', \'TorchRec\', \'TorchServe\', \'PyTorch on XLA Devices\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/pytorchvideo\', \'slow_r50\', pretrained=True)", "device = \'cpu\'", \'model = model.eval()\', \'model = model.to(device)\'], \'performance\': {\'dataset\': \'Kinetics 400\', \'accuracy\': {\'top_1\': 74.58, \'top_5\': 91.63}, \'Flops (G)\': 54.52, \'Params (M)\': 32.45}, \'description\': "The 3D ResNet model is a Resnet-style video classification network pretrained on the Kinetics 400 dataset. It is based on the architecture from the paper \'SlowFast Networks for Video Recognition\' by Christoph Feichtenhofer et al."}', metadata={})]


 A video editor is developing a software that will allow users to mute specific instruments in a song. Provide an API that can separate audio into multiple tracks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Separation\', \'framework\': \'PyTorch\', \'functionality\': \'Music Source Separation\', \'api_name\': \'Open-Unmix\', \'api_call\': "torch.hub.load(repo_or_dir=\'sigsep/open-unmix-pytorch\', model=\'umxhq\', pretrained=True)", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'PyTorch >=1.6.0\', \'torchaudio\'], \'example_code\': [\'import torch\', "separator = torch.hub.load(\'sigsep/open-unmix-pytorch\', \'umxhq\')", \'audio = torch.rand((1, 2, 100000))\', \'original_sample_rate = separator.sample_rate\', \'estimates = separator(audio)\'], \'performance\': {\'dataset\': \'MUSDB18\', \'accuracy\': \'N/A\'}, \'description\': \'Open-Unmix provides ready-to-use models that allow users to separate pop music into four stems: vocals, drums, bass and the remaining other instruments. The models were pre-trained on the freely available MUSDB18 dataset.\'}', metadata={})]


 I am working on a project where I need to convert a text document into an audio file. Can you suggest an API for text-to-speech conversion?\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={})]


 Suggest an API for identifying objects in a picture taken at a city park.\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 have an image and I need to detect the different objects in that image. Give me an API that can do 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={})]


 I want to create a new collection of clothing designs. Recommend an API that can generate unique images of clothing items.\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'm working on an image classification project where I need to identify the contents of an image. Can you suggest an API for that?\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={})]


 List an API that will allow me to input text that will be transformed into an audio file.\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={})]


 Find a model that is optimal for the task of person re-identification from a set of images.\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={})]


 Query an API that carries out vehicle or person re-identification tasks accurately.\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 image classification model that can classify objects in images with high accuracy. Suggest me an API.\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={})]


 Help me find a way to classify different species of birds given an image from the Internet.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Fine-grained image classifier\', \'api_name\': \'ntsnet\', \'api_call\': "torch.hub.load(repo_or_dir=\'nicolalandro/ntsnet-cub200\', model=\'ntsnet\', pretrained=True, **{\'topN\': 6, \'device\':\'cpu\', \'num_classes\': 200})", \'api_arguments\': {\'pretrained\': \'True\', \'topN\': \'6\', \'device\': \'cpu\', \'num_classes\': \'200\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': "from torchvision import transforms\\nimport torch\\nimport urllib\\nfrom PIL import Image\\n\\ntransform_test = transforms.Compose([\\n transforms.Resize((600, 600), Image.BILINEAR),\\n transforms.CenterCrop((448, 448)),\\n transforms.ToTensor(),\\n transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\\n])\\n\\nmodel = torch.hub.load(\'nicolalandro/ntsnet-cub200\', \'ntsnet\', pretrained=True, **{\'topN\': 6, \'device\':\'cpu\', \'num_classes\': 200})\\nmodel.eval()\\n\\nurl = \'https://raw.githubusercontent.com/nicolalandro/ntsnet-cub200/master/images/nts-net.png\'\\nimg = Image.open(urllib.request.urlopen(url))\\nscaled_img = transform_test(img)\\ntorch_images = scaled_img.unsqueeze(0)\\n\\nwith torch.no_grad():\\n top_n_coordinates, concat_out, raw_logits, concat_logits, part_logits, top_n_index, top_n_prob = model(torch_images)\\n\\n_, predict = torch.max(concat_logits, 1)\\npred_id = predict.item()\\nprint(\'bird class:\', model.bird_classes[pred_id])", \'performance\': {\'dataset\': \'CUB200 2011\', \'accuracy\': \'Not provided\'}, \'description\': \'This is an nts-net pretrained with CUB200 2011 dataset, which is a fine-grained dataset of birds species.\'}', metadata={})]


 Your pet store is building a new image classifier for the different types of pets. Tell me which API can identify the breeds given pet 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={})]


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


 I'm a photographer and I need to classify images according to their category. Write the code to use a machine learning API to achieve that.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SNNMLP\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/Efficient-AI-Backbones\', model=\'snnmlp_s\', pretrained=True)", \'api_arguments\': [{\'name\': \'snnmlp_s\', \'type\': \'str\', \'description\': \'SNNMLP Small model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/Efficient-AI-Backbones\', \'snnmlp_s\', 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)\', \'print(torch.nn.functional.softmax(output[0], dim=0))\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'SNNMLP Small\', \'top-1\': 83.3}}, \'description\': \'SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively.\'}', metadata={})]


 I want to classify images accurately without latency. Help me find an API to do that.\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={})]


 Imagine I am an app developer and need to build Instagram like app that can classify user's images for easy searching lateron. Please suggest a pre-trained AI API that can help me in my endeavors.\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 retailer would like to better categorize images of products on their website. Provide a model API that can perform 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={})]


 Tesla wants to improve the back camera of their cars, and they are seeking an API for jointly handling object detection, drivable area segmentation, and lane detection. Provide a suitable API for 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 need a Python library for calculating relative depth from a single image. What do you suggest?\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={})]


 Tell me an API that I can use to classify images into different categories using a pre-trained ResNet50 model.\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={})]


 I am developing an app for bird species classification. Suggest me an API that can identify bird species in images.\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 need to analyze aerial images of agricultural fields to identify specific crop types. Can you suggest an API for classifying the crops in the images?\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={})]


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


 An AI engineer is searching for an API capable of image classification. Please provide an SDK that uses a pre-trained model for image recognition tasks.\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={})]


 Tell me an API that can predict the breed of a dog through its image.\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={})]


 A wildlife researcher wants to identify different bird species from a picture. Suggest a deep learning model that can help them achieve this.\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={})]


 What type of model is best for recognizing multiple objects in 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={})]


 Find the species of an animal in a given photo using an API.\n###Input: \"zebra.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={})]


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


 Provide an API for converting text to speech, since the marketing team needs to generate realistic voices for a series of advertisements.\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 need an API that helps classify images with the highest accuracy. Tell me an API that can achieve 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={})]


 Pinterest wants to build a system that can categorize images uploaded by users. What API should they use for this task?\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={})]


 Recommend me an API that can compute a depth map from a single 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_Hybrid\', 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_Hybrid\')", "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={})]


 I am working on a project that involves bird image identification. Can you recommend an API that can classify bird species from images?\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={})]


 Suggest an object detection API that is suitable for implementing real-time applications like a security camera.\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 mobile application needs a machine learning model for object classification from various user images. Suggest an appropriate API 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={})]


 I have a dataset with labeled images of clothing items from several fashion brands, and I want to classify them by brand. Which API can help me perform a classification task?\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={})]


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


 I want to classify some images using a state-of-the-art model. Can you provide me an API to help in 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={})]


 Show me an API that can efficiently classify images on mobile platforms.\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={})]


 We are developing an app that can guess the type of a picture. We need it to work on most platforms with almost the same efficiency. Give me an API that can do it.\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={})]


 A company wants to develop a photo sharing app like Instagram. Recommend an API to recognize objects in the photos uploaded by 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={})]


 Google Photos wants to create a way to classify images uploaded by users into different categories. Recommend an API for this purpose.\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={})]


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


 Is there any API that can identify plants from an image I provide?\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 mobile app developer needs an image classification API that can be used on a range of mobile devices without the need to adjust the model size. Recommend an API that fits this purpose.\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'm building an image classification app to classify animals. Tell me an API that can classify an input image into a specific category.\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_0\', pretrained=True)", \'api_arguments\': {\'version\': \'v0.10.0\', \'model\': [\'squeezenet1_0\'], \'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_0\', 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_0\': {\'Top-1 error\': 41.9, \'Top-5 error\': 19.58}}}, \'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={})]


 I want to create a 3D visualization of a room using only a single image. How can I estimate the depth of the objects in the room from that 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={})]


 Give me an API that can predict the category of an object given its 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 provide a GAN API that can generate high-quality 64x64 images for an apparel ecommerce company?\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 a city planner responsible for managing different areas of the city. Recommend an API that can segment roads, parks and buildings from a satellite 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={})]


 Recommend an API that can be used for bird species recognition using pictures taken by a wildlife photographer.\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 starting a startup that recommends clothing to users based on images of their outfits. What is a good API for this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Generative Adversarial Networks (GANs)\', \'api_name\': \'PGAN\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorch_GAN_zoo:hub\', model=\'PGAN\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'facebookresearch/pytorch_GAN_zoo:hub\', \'model\': \'PGAN\', \'model_name\': \'celebAHQ-512\', \'pretrained\': \'True\', \'useGPU\': \'use_gpu\'}, \'python_environment_requirements\': \'Python 3\', \'example_code\': {\'import\': \'import torch\', \'use_gpu\': \'use_gpu = True if torch.cuda.is_available() else False\', \'load_model\': "model = torch.hub.load(\'facebookresearch/pytorch_GAN_zoo:hub\', \'PGAN\', model_name=\'celebAHQ-512\', pretrained=True, useGPU=use_gpu)", \'build_noise_data\': \'noise, _ = model.buildNoiseData(num_images)\', \'test\': \'generated_images = model.test(noise)\', \'plot_images\': {\'import_matplotlib\': \'import matplotlib.pyplot as plt\', \'import_torchvision\': \'import torchvision\', \'make_grid\': \'grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)\', \'imshow\': \'plt.imshow(grid.permute(1, 2, 0).cpu().numpy())\', \'show\': \'plt.show()\'}}, \'performance\': {\'dataset\': \'celebA\', \'accuracy\': \'High-quality celebrity faces\'}, \'description\': "Progressive Growing of GANs (PGAN) is a method for generating high-resolution images using generative adversarial networks. The model is trained progressively, starting with low-resolution images and gradually increasing the resolution until the desired output is achieved. This implementation is based on the paper by Tero Karras et al., \'Progressive Growing of GANs for Improved Quality, Stability, and Variation\'."}', metadata={})]