 Identify an API which detects voice activity in an audio file and share the code to load it.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Voice Activity Detection\', \'api_name\': \'Silero Voice Activity Detector\', \'api_call\': "torch.hub.load(repo_or_dir=\'snakers4/silero-vad\', model=\'silero_vad\', force_reload=True)", \'api_arguments\': {\'repo_or_dir\': \'snakers4/silero-vad\', \'model\': \'silero_vad\', \'force_reload\': \'True\'}, \'python_environment_requirements\': {\'torchaudio\': \'pip install -q torchaudio\'}, \'example_code\': {\'import\': [\'import torch\', \'torch.set_num_threads(1)\', \'from IPython.display import Audio\', \'from pprint import pprint\'], \'download_example\': "torch.hub.download_url_to_file(\'https://models.silero.ai/vad_models/en.wav\', \'en_example.wav\')", \'load_model\': "model, utils = torch.hub.load(repo_or_dir=\'snakers4/silero-vad\', model=\'silero_vad\', force_reload=True)", \'load_utils\': \'(get_speech_timestamps, _, read_audio, _) = utils\', \'set_sampling_rate\': \'sampling_rate = 16000\', \'read_audio\': "wav = read_audio(\'en_example.wav\', sampling_rate=sampling_rate)", \'get_speech_timestamps\': \'speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=sampling_rate)\', \'print_speech_timestamps\': \'pprint(speech_timestamps)\'}, \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Silero VAD is a pre-trained enterprise-grade Voice Activity Detector (VAD) that aims to provide a high-quality and modern alternative to the WebRTC Voice Activity Detector. The model is optimized for performance on 1 CPU thread and is quantized.\'}', metadata={})]


 Help me identify various objects in an image. Suggest an API for performing image classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 A marketing company needs an API to classify images into animals and assign them different categories. Which API would you recommend them?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 Recommend an API for a mobile app that can identify fruits from images taken by the users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MEAL_V2\', \'api_call\': "torch.hub.load(repo_or_dir=\'szq0214/MEAL-V2\', \'meal_v2\', model=\'mealv2_mobilenetv3_small_100\', pretrained=True)", \'api_arguments\': {\'model_name\': \'mealv2_mobilenetv3_small_100\'}, \'python_environment_requirements\': \'!pip install timm\', \'example_code\': "import torch\\nfrom PIL import Image\\nfrom torchvision import transforms\\n\\nmodel = torch.hub.load(\'szq0214/MEAL-V2\',\'meal_v2\', \'mealv2_resnest50_cutmix\', pretrained=True)\\nmodel.eval()\\n\\ninput_image = Image.open(\'dog.jpg\')\\npreprocess = transforms.Compose([\\n transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\n])\\ninput_tensor = preprocess(input_image)\\ninput_batch = input_tensor.unsqueeze(0)\\n\\nif torch.cuda.is_available():\\n input_batch = input_batch.to(\'cuda\')\\n model.to(\'cuda\')\\n\\nwith torch.no_grad():\\n output = model(input_batch)\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\nprint(probabilities)", \'performance\': [{\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'MEAL-V2 w/ MobileNet V3-Small 1.0\', \'resolution\': \'224\', \'parameters\': \'2.54M\', \'top1\': \'69.65\', \'top5\': \'88.71\'}}], \'description\': \'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\'}', metadata={})]


 A city is planning to survey the land for urban development. Provide me with an API that can identify buildings and roads from an aerial photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'HybridNets\', \'api_call\': "torch.hub.load(repo_or_dir=\'datvuthanh/hybridnets\', model=\'hybridnets\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'Python>=3.7, PyTorch>=1.10\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'datvuthanh/hybridnets\', \'hybridnets\', pretrained=True)\\nimg = torch.randn(1,3,640,384)\\nfeatures, regression, classification, anchors, segmentation = model(img)", \'performance\': {\'dataset\': [{\'name\': \'BDD100K\', \'accuracy\': {\'Traffic Object Detection\': {\'Recall (%)\': 92.8, \'mAP@0.5 (%)\': 77.3}, \'Drivable Area Segmentation\': {\'Drivable mIoU (%)\': 90.5}, \'Lane Line Detection\': {\'Accuracy (%)\': 85.4, \'Lane Line IoU (%)\': 31.6}}}]}, \'description\': \'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\'}', metadata={})]


 I need an efficient model for classifying animals in images taken by wildlife cameras. Suggest me an API for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MEAL_V2\', \'api_call\': "torch.hub.load(repo_or_dir=\'szq0214/MEAL-V2\', \'meal_v2\', model=\'mealv2_efficientnet_b0\', pretrained=True)", \'api_arguments\': {\'model_name\': \'mealv2_efficientnet_b0\'}, \'python_environment_requirements\': \'!pip install timm\', \'example_code\': "import torch\\nfrom PIL import Image\\nfrom torchvision import transforms\\n\\nmodel = torch.hub.load(\'szq0214/MEAL-V2\',\'meal_v2\', \'mealv2_resnest50_cutmix\', pretrained=True)\\nmodel.eval()\\n\\ninput_image = Image.open(\'dog.jpg\')\\npreprocess = transforms.Compose([\\n transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\n])\\ninput_tensor = preprocess(input_image)\\ninput_batch = input_tensor.unsqueeze(0)\\n\\nif torch.cuda.is_available():\\n input_batch = input_batch.to(\'cuda\')\\n model.to(\'cuda\')\\n\\nwith torch.no_grad():\\n output = model(input_batch)\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\nprint(probabilities)", \'performance\': [{\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'MEAL-V2 w/ EfficientNet-B0\', \'resolution\': \'224\', \'parameters\': \'5.29M\', \'top1\': \'78.29\', \'top5\': \'93.95\'}}], \'description\': \'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\'}', metadata={})]


 The company is creating a neural network model that can run efficiently on different hardware platforms. Tell me an API that specializes CNNs for different hardware.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'EfficientNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_efficientnet_b0\', pretrained=True)", \'api_arguments\': [\'model_name\', \'pretrained\'], \'python_environment_requirements\': [\'validators\', \'matplotlib\'], \'example_code\': "import torch\\n\\nefficientnet = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_efficientnet_b0\', pretrained=True)\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_convnets_processing_utils\')\\n\\nefficientnet.eval().to(device)\\n\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\n\\nwith torch.no_grad():\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\n \\nresults = utils.pick_n_best(predictions=output, n=5)", \'performance\': {\'dataset\': {\'name\': \'IMAGENET\', \'accuracy\': \'Not provided\'}}, \'description\': \'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\'}', metadata={})]


 Farlando Corp has an application that runs on their customers' GPUs, and they want a neural network that is optimized on GPU performance. Recommend an API that they can use for image classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'HarDNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'PingoLH/Pytorch-HarDNet\', model=\'hardnet85\', pretrained=True)", \'api_arguments\': [{\'name\': \'hardnet85\', \'type\': \'str\', \'description\': \'HarDNet-85 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'PingoLH/Pytorch-HarDNet\', \'hardnet85\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'hardnet85\': {\'Top-1 error\': 21.96, \'Top-5 error\': 6.11}}}, \'description\': \'Harmonic DenseNet (HarDNet) is a low memory traffic CNN model, which is fast and efficient. The basic concept is to minimize both computational cost and memory access cost at the same time, such that the HarDNet models are 35% faster than ResNet running on GPU comparing to models with the same accuracy (except the two DS models that were designed for comparing with MobileNet).\'}', metadata={})]


 I need an efficient model for image classification with good accuracy. Provide me with an API that uses LIF neurons.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SNNMLP\', \'api_call\': "torch.hub.load(repo_or_dir=\'huawei-noah/Efficient-AI-Backbones\', model=\'snnmlp_b\', pretrained=True)", \'api_arguments\': [{\'name\': \'snnmlp_b\', \'type\': \'str\', \'description\': \'SNNMLP Base model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'huawei-noah/Efficient-AI-Backbones\', \'snnmlp_b\', 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 Base\', \'top-1\': 85.59}}, \'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={})]


 As a market research analyst, I want to find a tool to classify different product types using their images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MEAL_V2\', \'api_call\': "torch.hub.load(repo_or_dir=\'szq0214/MEAL-V2\', \'meal_v2\', model=\'mealv2_resnest50_cutmix\', pretrained=True)", \'api_arguments\': {\'model_name\': \'mealv2_resnest50_cutmix\'}, \'python_environment_requirements\': \'!pip install timm\', \'example_code\': "import torch\\nfrom PIL import Image\\nfrom torchvision import transforms\\n\\nmodel = torch.hub.load(\'szq0214/MEAL-V2\',\'meal_v2\', \'mealv2_resnest50_cutmix\', pretrained=True)\\nmodel.eval()\\n\\ninput_image = Image.open(\'dog.jpg\')\\npreprocess = transforms.Compose([\\n transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\n])\\ninput_tensor = preprocess(input_image)\\ninput_batch = input_tensor.unsqueeze(0)\\n\\nif torch.cuda.is_available():\\n input_batch = input_batch.to(\'cuda\')\\n model.to(\'cuda\')\\n\\nwith torch.no_grad():\\n output = model(input_batch)\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\nprint(probabilities)", \'performance\': [{\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'MEAL-V2 + CutMix w/ ResNet50\', \'resolution\': \'224\', \'parameters\': \'25.6M\', \'top1\': \'80.98\', \'top5\': \'95.35\'}}], \'description\': \'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\'}', metadata={})]


 A media company that works with image recognition is trying to identify an object in an image. Recommend an API that specializes in image recognition.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 Inform me of an API that can help identify famous landmarks from images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 I am working on an image classification project where accuracy is important, and I need a pretrained model that has a lower error rate when classifying images. What model might work for me?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 The New York Times wants to classify some information about Jim Henson. Recommend an API to analyze and classify the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Natural Language Processing\', \'api_name\': \'PyTorch-Transformers\', \'api_call\': "torch.hub.load(repo_or_dir=\'huggingface/pytorch-transformers\')", \'api_arguments\': [\'pretrained_model_or_path\', \'output_attention\', \'output_hidden_states\', \'config\', \'from_tf\'], \'python_environment_requirements\': [\'tqdm\', \'boto3\', \'requests\', \'regex\', \'sentencepiece\', \'sacremoses\'], \'example_code\': \'import torch\\ntokenizer = torch.hub.load(\\\'huggingface/pytorch-transformers\\\', \\\'tokenizer\\\', \\\'bert-base-cased\\\')\\n\\ntext_1 = "Jim Henson was a puppeteer"\\ntext_2 = "Who was Jim Henson ?"\\n\\nindexed_tokens = tokenizer.encode(text_1, text_2, add_special_tokens=True)\\n\\nmodel = torch.hub.load(\\\'huggingface/pytorch-transformers\\\', \\\'model\\\', \\\'bert-base-cased\\\')\\n\\nwith torch.no_grad():\\n encoded_layers, _ = model(tokens_tensor, token_type_ids=segments_tensors)\', \'performance\': {\'dataset\': [{\'name\': \'MRPC\', \'accuracy\': \'Not provided\'}]}, \'description\': \'PyTorch-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP) including BERT, GPT, GPT-2, Transformer-XL, XLNet, XLM, RoBERTa, and DistilBERT. The library provides functionality for tokenization, configuration, and various model architectures for different tasks such as causal language modeling, sequence classification, question answering, and masked language modeling.\'}', metadata={})]


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


 I have a picture of my dog and I want to classify its breed. Provide me an API to do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13_bn\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13_bn\', \'type\': \'str\', \'description\': \'VGG13 model with batch normalization\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13_bn\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13_bn\': {\'Top-1 error\': 28.45, \'Top-5 error\': 9.63}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 A developer at Pinterest wants to automatically categorize uploaded images based on their content. Provide an API suggestion that can help with this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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 startup is working on a computer vision application supporting autonomous drones. Can you provide an API that can compute the relative depth of an object in a given image?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Imagine you are trying to build podcast transcription for people who are impaired. Get an API to transcribe a sample podcast from Spotify.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text-To-Speech\', \'framework\': \'PyTorch\', \'functionality\': \'Text-To-Speech\', \'api_name\': \'Silero Text-To-Speech Models\', \'api_call\': "torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'snakers4/silero-models\', \'model\': \'silero_tts\', \'language\': \'language\', \'speaker\': \'speaker\'}, \'python_environment_requirements\': [\'pip install -q torchaudio omegaconf\'], \'example_code\': "import torch\\nlanguage = \'en\'\\nspeaker = \'lj_16khz\'\\ndevice = torch.device(\'cpu\')\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', language=language, speaker=speaker)\\nmodel = model.to(device)\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)", \'performance\': {\'dataset\': [{\'language\': \'Russian\', \'speakers\': 6}, {\'language\': \'English\', \'speakers\': 1}, {\'language\': \'German\', \'speakers\': 1}, {\'language\': \'Spanish\', \'speakers\': 1}, {\'language\': \'French\', \'speakers\': 1}], \'accuracy\': \'High throughput on slow hardware. Decent performance on one CPU thread\'}, \'description\': \'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\'}', metadata={})]


 A tourist is planning to take a picture of a beautiful scene but wants to separate the people from the background. Recommend an API to help do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 I took a photo and I want to detect all the objects in the image. Provide me with an API to do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Find an API that can generate new images of various clothing styles in 64x64 resolution using Generative Adversarial Networks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Generative Adversarial Networks (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={})]


 I am trying to classify an image to find its category. Please give me an API that can identify the content of an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 I would like to convert text to natural sounding speech using Deep Learning. Can you provide me with an API to achieve this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text-To-Speech\', \'framework\': \'PyTorch\', \'functionality\': \'Text-To-Speech\', \'api_name\': \'Silero Text-To-Speech Models\', \'api_call\': "torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'snakers4/silero-models\', \'model\': \'silero_tts\', \'language\': \'language\', \'speaker\': \'speaker\'}, \'python_environment_requirements\': [\'pip install -q torchaudio omegaconf\'], \'example_code\': "import torch\\nlanguage = \'en\'\\nspeaker = \'lj_16khz\'\\ndevice = torch.device(\'cpu\')\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', language=language, speaker=speaker)\\nmodel = model.to(device)\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)", \'performance\': {\'dataset\': [{\'language\': \'Russian\', \'speakers\': 6}, {\'language\': \'English\', \'speakers\': 1}, {\'language\': \'German\', \'speakers\': 1}, {\'language\': \'Spanish\', \'speakers\': 1}, {\'language\': \'French\', \'speakers\': 1}], \'accuracy\': \'High throughput on slow hardware. Decent performance on one CPU thread\'}, \'description\': \'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\'}', metadata={})]


 Design a system to diagnose diseases from X-Ray images. Recommend an appropriate API for classifying diseases in the X-Ray images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'ResNext\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'resnext50_32x4d\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\'], \'example_code\': [\'import torch\', \'from PIL import Image\', \'from torchvision import transforms\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'resnext50_32x4d\', pretrained=True)", \'model.eval()\', "input_image = Image.open(\'dog.jpg\')", \'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)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'resnext50_32x4d\': {\'top-1\': 22.38, \'top-5\': 6.3}}}, \'description\': \'ResNext is a next-generation ResNet architecture for image classification. It is more efficient and accurate than the original ResNet. This implementation includes two versions of the model, resnext50_32x4d and resnext101_32x8d, with 50 and 101 layers respectively.\'}', metadata={})]


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


 I want to create an app that recognizes items from pictures taken by users. Can you recommend any machine learning API for this purpose?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MobileNet v2\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'mobilenet_v2\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision\', \'mobilenet_v2\', 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)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 28.12, \'top-5_error\': 9.71}}, \'description\': \'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\'}', metadata={})]


 Recommend an API that can be used for image classification tasks on a dataset of images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg16_bn\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg16_bn\', \'type\': \'str\', \'description\': \'VGG16 model with batch normalization\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg16_bn\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg16_bn\': {\'Top-1 error\': 26.63, \'Top-5 error\': 8.5}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 Find out an API that can identify 102 different types of flowers from an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MEAL_V2\', \'api_call\': "torch.hub.load(repo_or_dir=\'szq0214/MEAL-V2\', \'meal_v2\', model=\'mealv2_resnest50\', pretrained=True)", \'api_arguments\': {\'model_name\': \'mealv2_resnest50\'}, \'python_environment_requirements\': \'!pip install timm\', \'example_code\': "import torch\\nfrom PIL import Image\\nfrom torchvision import transforms\\n\\nmodel = torch.hub.load(\'szq0214/MEAL-V2\',\'meal_v2\', \'mealv2_resnest50_cutmix\', pretrained=True)\\nmodel.eval()\\n\\ninput_image = Image.open(\'dog.jpg\')\\npreprocess = transforms.Compose([\\n transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\n])\\ninput_tensor = preprocess(input_image)\\ninput_batch = input_tensor.unsqueeze(0)\\n\\nif torch.cuda.is_available():\\n input_batch = input_batch.to(\'cuda\')\\n model.to(\'cuda\')\\n\\nwith torch.no_grad():\\n output = model(input_batch)\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\nprint(probabilities)", \'performance\': [{\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'MEAL-V2 w/ ResNet50\', \'resolution\': \'224\', \'parameters\': \'25.6M\', \'top1\': \'80.67\', \'top5\': \'95.09\'}}], \'description\': \'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\'}', metadata={})]


 Can you recommend an API for image classification which is efficient in terms of computational resources and has decent accuracy?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'EfficientNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_efficientnet_b0\', pretrained=True)", \'api_arguments\': [\'model_name\', \'pretrained\'], \'python_environment_requirements\': [\'validators\', \'matplotlib\'], \'example_code\': "import torch\\n\\nefficientnet = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_efficientnet_b0\', pretrained=True)\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_convnets_processing_utils\')\\n\\nefficientnet.eval().to(device)\\n\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\n\\nwith torch.no_grad():\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\n \\nresults = utils.pick_n_best(predictions=output, n=5)", \'performance\': {\'dataset\': {\'name\': \'IMAGENET\', \'accuracy\': \'Not provided\'}}, \'description\': \'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\'}', metadata={})]


 A photography service needs a fast algorithm to recognize objects in their images from the ImageNet dataset out of the box. What API should they use?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Can you suggest an API for classifying images in my dataset using a model with spiking neural networks?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'ShuffleNet v2\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'shufflenet_v2_x1_0\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'torch\', \'torchvision\': \'torchvision\', \'PIL\': \'Image\', \'urllib\': \'urllib\'}, \'example_code\': {\'import_libraries\': [\'import torch\', \'from PIL import Image\', \'from torchvision import transforms\', \'import urllib\'], \'load_model\': ["model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'shufflenet_v2_x1_0\', pretrained=True)", \'model.eval()\'], \'load_image\': ["url, filename = (\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\', \'dog.jpg\')", \'try: urllib.URLopener().retrieve(url, filename)\', \'except: urllib.request.urlretrieve(url, filename)\', \'input_image = Image.open(filename)\'], \'preprocess_image\': [\'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)\'], \'run_inference\': [\'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\'], \'get_probabilities\': [\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\'], \'top_categories\': [\'top5_prob, top5_catid = torch.topk(probabilities, 5)\', \'for i in range(top5_prob.size(0)):\', \' print(categories[top5_catid[i]], top5_prob[i].item())\']}, \'performance\': {\'dataset\': \'Imagenet\', \'accuracy\': {\'top-1_error\': 30.64, \'top-5_error\': 11.68}}, \'description\': \'ShuffleNet V2 is an efficient ConvNet optimized for speed and memory, pre-trained on Imagenet. It is designed based on practical guidelines for efficient network design, including speed and accuracy tradeoff.\'}', metadata={})]


 I am trying to recognize objects in an image using a popular image classification model. Which model should I use?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg16_bn\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg16_bn\', \'type\': \'str\', \'description\': \'VGG16 model with batch normalization\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg16_bn\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg16_bn\': {\'Top-1 error\': 26.63, \'Top-5 error\': 8.5}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 I want to create an app to recognize objects in images. Which API is suitable for this task?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Air Traffic Control needs an image classifier to identify if an image contains an aircraft or not. Suggest an API that would be suitable for this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 A smart fridge wants to identify food items from images taken from its camera. Provide an API to identify the food items.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MEAL_V2\', \'api_call\': "torch.hub.load(repo_or_dir=\'szq0214/MEAL-V2\', \'meal_v2\', model=\'mealv2_mobilenetv3_small_100\', pretrained=True)", \'api_arguments\': {\'model_name\': \'mealv2_mobilenetv3_small_100\'}, \'python_environment_requirements\': \'!pip install timm\', \'example_code\': "import torch\\nfrom PIL import Image\\nfrom torchvision import transforms\\n\\nmodel = torch.hub.load(\'szq0214/MEAL-V2\',\'meal_v2\', \'mealv2_resnest50_cutmix\', pretrained=True)\\nmodel.eval()\\n\\ninput_image = Image.open(\'dog.jpg\')\\npreprocess = transforms.Compose([\\n transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\n])\\ninput_tensor = preprocess(input_image)\\ninput_batch = input_tensor.unsqueeze(0)\\n\\nif torch.cuda.is_available():\\n input_batch = input_batch.to(\'cuda\')\\n model.to(\'cuda\')\\n\\nwith torch.no_grad():\\n output = model(input_batch)\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\nprint(probabilities)", \'performance\': [{\'dataset\': \'ImageNet\', \'accuracy\': {\'model\': \'MEAL-V2 w/ MobileNet V3-Small 1.0\', \'resolution\': \'224\', \'parameters\': \'2.54M\', \'top1\': \'69.65\', \'top5\': \'88.71\'}}], \'description\': \'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\'}', metadata={})]


 I want to count how many people are present in a room using an image. Tell me an API that can do this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'ResNext WSL\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/WSL-Images\', model=\'resnext101_32x16d_wsl\', pretrained=True)", \'api_arguments\': [{\'name\': \'resnext101_32x16d_wsl\', \'type\': \'str\', \'description\': \'ResNeXt-101 32x16d WSL model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/WSL-Images\', \'resnext101_32x16d_wsl\')", \'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(output[0])\', \'print(torch.nn.functional.softmax(output[0], dim=0))\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'ResNeXt-101 32x16d\': {\'Top-1 Acc.\': \'84.2\', \'Top-5 Acc.\': \'97.2\'}}}, \'description\': \'The provided ResNeXt models are pre-trained in weakly-supervised fashion on 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. The models significantly improve the training accuracy on ImageNet compared to training from scratch. They achieve state-of-the-art accuracy of 85.4% on ImageNet with the ResNext-101 32x48d model.\'}', metadata={})]


 I am developing a website that can predict the content of an image based on its URL. What API would you recommend with a code example?\n###Input: {\"image_url\": \"https://example.com/image.jpg\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


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


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


 I am building an image classification model and want to achieve a high accuracy. Which API should I use?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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 photographer at a film studio wants to find the relative depth from a single image. Recommend an API that can compute relative depth from an input image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Computing relative depth from a single image\', \'api_name\': \'MiDaS\', \'api_call\': "torch.hub.load(repo_or_dir=\'intel-isl/MiDaS\', model=\'DPT_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={})]


 A bird watching society is developing an app that can identify birds in a picture. Provide a suitable API that can be used for classifying birds from images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13_bn\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13_bn\', \'type\': \'str\', \'description\': \'VGG13 model with batch normalization\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13_bn\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13_bn\': {\'Top-1 error\': 28.45, \'Top-5 error\': 9.63}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 Provide an API recommendation for a call center which wants to convert customer voice calls into text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text-To-Speech\', \'framework\': \'PyTorch\', \'functionality\': \'Text-To-Speech\', \'api_name\': \'Silero Text-To-Speech Models\', \'api_call\': "torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'snakers4/silero-models\', \'model\': \'silero_tts\', \'language\': \'language\', \'speaker\': \'speaker\'}, \'python_environment_requirements\': [\'pip install -q torchaudio omegaconf\'], \'example_code\': "import torch\\nlanguage = \'en\'\\nspeaker = \'lj_16khz\'\\ndevice = torch.device(\'cpu\')\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', language=language, speaker=speaker)\\nmodel = model.to(device)\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)", \'performance\': {\'dataset\': [{\'language\': \'Russian\', \'speakers\': 6}, {\'language\': \'English\', \'speakers\': 1}, {\'language\': \'German\', \'speakers\': 1}, {\'language\': \'Spanish\', \'speakers\': 1}, {\'language\': \'French\', \'speakers\': 1}], \'accuracy\': \'High throughput on slow hardware. Decent performance on one CPU thread\'}, \'description\': \'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\'}', metadata={})]


 Provide me with an API that can tackle city-scape segmentation in autonomous driving application.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'HybridNets\', \'api_call\': "torch.hub.load(repo_or_dir=\'datvuthanh/hybridnets\', model=\'hybridnets\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'Python>=3.7, PyTorch>=1.10\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'datvuthanh/hybridnets\', \'hybridnets\', pretrained=True)\\nimg = torch.randn(1,3,640,384)\\nfeatures, regression, classification, anchors, segmentation = model(img)", \'performance\': {\'dataset\': [{\'name\': \'BDD100K\', \'accuracy\': {\'Traffic Object Detection\': {\'Recall (%)\': 92.8, \'mAP@0.5 (%)\': 77.3}, \'Drivable Area Segmentation\': {\'Drivable mIoU (%)\': 90.5}, \'Lane Line Detection\': {\'Accuracy (%)\': 85.4, \'Lane Line IoU (%)\': 31.6}}}]}, \'description\': \'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\'}', metadata={})]


 I need an API to extract features from a collection of photographs taken at the 2022 Olympics.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 I need an API to classify images with known objects. Suggest a suitable model that can do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]


 A delivery company wants to recognize if a package is damaged during shipment. Propose an API that can classify images into damaged and undamaged packages.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


 Show me an API that provides easy to use neural networks for classifying different types of wildlife on mobile platforms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MobileNet v2\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'mobilenet_v2\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision\', \'mobilenet_v2\', 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)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 28.12, \'top-5_error\': 9.71}}, \'description\': \'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\'}', metadata={})]


 Recommend an API for identifying defective parts in a manufacturing assembly line based on images taken by an inspection system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 I need an API to classify images efficiently without sacrificing too much accuracy. Can you provide me with one?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


 I need an efficient API to classify images on multiple edge devices with different resource constraints. Suggest one for me.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'EfficientNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_efficientnet_b0\', pretrained=True)", \'api_arguments\': [\'model_name\', \'pretrained\'], \'python_environment_requirements\': [\'validators\', \'matplotlib\'], \'example_code\': "import torch\\n\\nefficientnet = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_efficientnet_b0\', pretrained=True)\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_convnets_processing_utils\')\\n\\nefficientnet.eval().to(device)\\n\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\n\\nwith torch.no_grad():\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\n \\nresults = utils.pick_n_best(predictions=output, n=5)", \'performance\': {\'dataset\': {\'name\': \'IMAGENET\', \'accuracy\': \'Not provided\'}}, \'description\': \'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\'}', metadata={})]


 I want my app to be able to read aloud the text for audiobooks. Can you suggest me an API for converting text to speech?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text-To-Speech\', \'framework\': \'PyTorch\', \'functionality\': \'Text-To-Speech\', \'api_name\': \'Silero Text-To-Speech Models\', \'api_call\': "torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'snakers4/silero-models\', \'model\': \'silero_tts\', \'language\': \'language\', \'speaker\': \'speaker\'}, \'python_environment_requirements\': [\'pip install -q torchaudio omegaconf\'], \'example_code\': "import torch\\nlanguage = \'en\'\\nspeaker = \'lj_16khz\'\\ndevice = torch.device(\'cpu\')\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\'snakers4/silero-models\', model=\'silero_tts\', language=language, speaker=speaker)\\nmodel = model.to(device)\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)", \'performance\': {\'dataset\': [{\'language\': \'Russian\', \'speakers\': 6}, {\'language\': \'English\', \'speakers\': 1}, {\'language\': \'German\', \'speakers\': 1}, {\'language\': \'Spanish\', \'speakers\': 1}, {\'language\': \'French\', \'speakers\': 1}], \'accuracy\': \'High throughput on slow hardware. Decent performance on one CPU thread\'}, \'description\': \'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\'}', metadata={})]


 An app wants to identify dog breeds from images taken by users. Recommend an API that can classify the dog breed given a photo of a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg13\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg13\', \'type\': \'str\', \'description\': \'VGG13 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg13\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg13\': {\'Top-1 error\': 30.07, \'Top-5 error\': 10.75}}}, \'description\': \'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\'}', metadata={})]