 What is an API that can be used to classify sports activities in videos?\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={})]


 Identify an API capable of converting spoken language in a recording to 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={})]


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


 To implement a lightweight object detection, I'm looking for a pre-trained model API that can detect and classify objects within an image in real-time.\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 API that can handle millions of public images with thousands of hashtags. Please recommend one.\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={})]


 Developers of a Virtual Reality event want to create a realistic digital crowd. Can you suggest a pretrained model to generate faces of celebrities?\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 need an API to classify images from a dataset with a high accuracy rate. Provide an appropriate API and the performance on the ImageNet dataset.\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 tourism website is building a feature to categorize photos into classes of landmarks. Recommend a machine learning API that will take an image and output which class the image falls into.\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 National Geographic is finding photos for the monthly magazine cover. They need a model to classify a picture of a cheetah running in the wild from other images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'GoogLeNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'googlenet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.0.0\', \'torchvision\': \'>=0.2.2\'}, \'example_code\': {\'import\': [\'import torch\', \'import urllib\', \'from PIL import Image\', \'from torchvision import transforms\'], \'load_model\': "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'googlenet\', pretrained=True)", \'model_eval\': \'model.eval()\', \'image_preprocessing\': [\'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)\'], \'model_execution\': [\'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\'], \'output_processing\': [\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'top5_prob, top5_catid = torch.topk(probabilities, 5)\']}, \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'Top-1 error\': \'30.22\', \'Top-5 error\': \'10.47\'}}, \'description\': "GoogLeNet is based on a deep convolutional neural network architecture codenamed \'Inception\', which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014)."}', metadata={})]


 DXmart needs to build a product image classification system for their e-commerce site. Provide an API that can classify product images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Dense Convolutional Network\', \'api_name\': \'Densenet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'densenet161\', pretrained=True)", \'api_arguments\': [{\'name\': \'densenet161\', \'type\': \'str\', \'description\': \'Densenet-161 model\'}], \'python_environment_requirements\': {\'torch\': \'latest\', \'torchvision\': \'latest\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'densenet161\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'densenet161\': {\'Top-1 error\': 22.35, \'Top-5 error\': 6.2}}}, \'description\': \'Dense Convolutional Network (DenseNet) connects each layer to every other layer in a feed-forward fashion. It alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and substantially reduces the number of parameters.\'}', metadata={})]


 Identify an API to perform efficient animal classification from user provided images without sacrificing model accuracy for a biodiversity conservation project.\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={})]


 Recommend an API to build an Image Classifier that would better classify images with minimal computational resources.\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={})]


 I need to recognize dogs and cats from images. What API should I use to perform 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=\'vgg16\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg16\', \'type\': \'str\', \'description\': \'VGG16 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg16\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg16\': {\'Top-1 error\': 28.41, \'Top-5 error\': 9.62}}}, \'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 need a suitable PyTorch API that can classify a wide range of images. Please provide me with instructions on how to load the pretrained model.\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 to build an image classifier to identify objects in a photo. Suggest a suitable model that I can use for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SqueezeNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'squeezenet1_1\', pretrained=True)", \'api_arguments\': {\'version\': \'v0.10.0\', \'model\': [\'squeezenet1_1\'], \'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'squeezenet1_1\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\', \'print(probabilities)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'squeezenet1_1\': {\'Top-1 error\': 41.81, \'Top-5 error\': 19.38}}}, \'description\': \'SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.\'}', metadata={})]


 A developer is building a mobile app to identify objects using the mobile camera. Suggest an API to classify object types given an image.\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={})]


 A wildlife organization is looking to classify photos taken on their CCTV cameras into 100 different animal species. Suggest an API to achieve 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 self-driving car company is developing an autonomous vehicle that requires detecting objects, drivable area segmentation, and lane detection in real-time. Suggest an appropriate API for this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'YOLOP\', \'api_call\': "torch.hub.load(repo_or_dir=\'hustvl/yolop\', model=\'yolop\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'hustvl/yolop\', \'yolop\', pretrained=True)\\nimg = torch.randn(1,3,640,640)\\ndet_out, da_seg_out,ll_seg_out = model(img)", \'performance\': {\'dataset\': \'BDD100K\', \'accuracy\': {\'Object Detection\': {\'Recall(%)\': 89.2, \'mAP50(%)\': 76.5, \'Speed(fps)\': 41}, \'Drivable Area Segmentation\': {\'mIOU(%)\': 91.5, \'Speed(fps)\': 41}, \'Lane Detection\': {\'mIOU(%)\': 70.5, \'IOU(%)\': 26.2}}}, \'description\': \'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\'}', metadata={})]


 I want an ML library that can determine the object distances in a photo without inputting more than one photo.\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 would like a simple method to turn spoken user commands into text, which AI API would you recommend?\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={})]


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


 I'm working on a medical app and I want to classify images of skin lesions. Show me an API that can classify images with high efficiency and accuracy.\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={})]


 What is an API that can classify an image of a dog into its specific breed from a list of 120 unique breeds?\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 give me an API that can classify food dishes in restaurant menus using image classification?\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={})]


 For my mobile app, I need an efficient and light-weight model that can classify animals, plants, landmarks, etc. in an image fed via the device's camera. Suggest an API.\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={})]


 For a wildlife photography website, suggest an API that can classify the animal species in a given photo.\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={})]


 Please suggest an API that can detect and count the number of birds in 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=\'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={})]


 Identify an API that can classify images and works with spiking neural networks.\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={})]


 What is an efficient API that can be used to categorize images and has a much lighter model with fewer parameters than AlexNet?\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={})]


 Find me an API which will help identifying animals in a given 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={})]


 My company is building a chatbot for a car dealership and we need a machine learning model that can classify cars from images. Can you suggest 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={})]


 A wildlife conservationist wants to classify animals in their natural habitat with a high accuracy. Recommend an API that can assist in 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 software engineer working at a computer vision company is looking for a model that can classify images efficiently on NVIDIA GPUs. Provide an API recommendation.\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={})]


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


 In an attempt to streamline content moderation, Facebook is implementing an AI-enabled tool to identify potentially inappropriate images. Suggest an API that can recognize objects within an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Semi-supervised and semi-weakly supervised ImageNet Models\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/semi-supervised-ImageNet1K-models\', model=\'resnet18_swsl\', pretrained=True)", \'api_arguments\': {\'repository\': \'facebookresearch/semi-supervised-ImageNet1K-models\', \'model\': \'resnet18_swsl\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/semi-supervised-ImageNet1K-models\', \'resnet18_swsl\', pretrained=True)", \'model.eval()\'], \'performance\': {\'description\': \'Semi-supervised and semi-weakly supervised ImageNet models achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture.\'}, \'description\': "Semi-supervised and semi-weakly supervised ImageNet Models are introduced in the \'Billion scale semi-supervised learning for image classification\' paper. These models are pretrained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset. They are capable of classifying images into different categories and are provided by the Facebook Research library."}', metadata={})]


 The weatherman needs an AI which could read out the daily weather information. Tell me an API that generates spoken weather information from a written weather forecast.\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 developer needs to classify images using a model that does not require additional tricks for high accuracy. Recommend an API with a high top-1 accuracy without using any tricks.\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 that can help me identify the type of a cucumber. It should be able to tell me whether it's pickling, slicing, or burpless cucumber.\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={})]


 I need to develop a self-driving car which can simultaneously recognize objects, drivable areas, and lanes. Recommend me an API to handle these tasks.\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'd like to detect voice activity in an audio file. What API can help me perform this task?\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={})]


 We wish to create an app to make coloring books from images. Recommend an API to extract the regions that should be colored.\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={})]


 Imagine you were given a set of images and you need to tell what objects are on the pictures. Indicate an API that can classify the objects in the 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={})]


 My friend recommended the Densenet-201 model to classify images. Find an API that I can use for this model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Dense Convolutional Network\', \'api_name\': \'Densenet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'densenet201\', pretrained=True)", \'api_arguments\': [{\'name\': \'densenet201\', \'type\': \'str\', \'description\': \'Densenet-201 model\'}], \'python_environment_requirements\': {\'torch\': \'latest\', \'torchvision\': \'latest\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'densenet201\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'densenet201\': {\'Top-1 error\': 22.8, \'Top-5 error\': 6.43}}}, \'description\': \'Dense Convolutional Network (DenseNet) connects each layer to every other layer in a feed-forward fashion. It alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and substantially reduces the number of parameters.\'}', metadata={})]


 Provide me with an API that can segment objects within an image into separate categories.\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={})]


 Looking for a fast and efficient image classification API to suit my low-end device. What would you recommend?\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 need a model that can help identify which domain an image belongs to, such as artistic style or natural scenery. Recommend me an API 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={})]


 I want to know which dog breed a given image belongs to. Tell me an API that is capable of identifying dog breeds.\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 need to classify images into various categories based on their content. Can you suggest an API that can do 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={})]


 Recommend an API to automatically fine-tune a neural network's architecture for optimal performance on a specific graphics processing unit (GPU) platform.\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={})]


 A software engineer is trying to determine if an image contains a dog, cat or a horse. Identify an API that could be fine-tuned to achieve the objective.\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={})]


 Can you suggest me an AI model that can classify images with 50x fewer parameters than AlexNet and better performance on a robotics project I'm working on?\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 a way to recognize decorative and architectural elements in architectural design images using a pre-trained network.\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={})]


 Can you suggest an API that can automatically classify images 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={})]


 Suggest an API for classifying dog breeds given an image 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=\'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={})]


 Suggest an API designed for NVIDIA GPU and TensorRT performance optimization to classify images into different categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'SE-ResNeXt101\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_se_resnext101_32x4d\', pretrained=True)", \'api_arguments\': \'N/A\', \'python_environment_requirements\': \'validators, matplotlib\', \'example_code\': "import torch\\nfrom PIL import Image\\nimport torchvision.transforms as transforms\\nimport numpy as np\\nimport json\\nimport requests\\nimport matplotlib.pyplot as plt\\nimport warnings\\nwarnings.filterwarnings(\'ignore\')\\n%matplotlib inline\\ndevice = torch.device(\'cuda\') if torch.cuda.is_available() else torch.device(\'cpu\')\\nprint(f\'Using {device} for inference\')\\nresneXt = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_se_resnext101_32x4d\')\\nutils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_convnets_processing_utils\')\\nresneXt.eval().to(device)\\nuris = [\'http://images.cocodataset.org/test-stuff2017/000000024309.jpg\',\'http://images.cocodataset.org/test-stuff2017/000000028117.jpg\',\'http://images.cocodataset.org/test-stuff2017/000000006149.jpg\',\'http://images.cocodataset.org/test-stuff2017/000000004954.jpg\']\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\nwith torch.no_grad():\\n output = torch.nn.functional.softmax(resneXt(batch), dim=1)\\nresults = utils.pick_n_best(predictions=output, n=5)\\nfor uri, result in zip(uris, results):\\n img = Image.open(requests.get(uri, stream=True).raw)\\n img.thumbnail((256,256), Image.ANTIALIAS)\\n plt.imshow(img)\\n plt.show()\\n print(result)", \'performance\': {\'dataset\': \'IMAGENET\', \'accuracy\': \'N/A\'}, \'description\': \'The SE-ResNeXt101-32x4d is a ResNeXt101-32x4d model with added Squeeze-and-Excitation module. This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures, which allows researchers to get results 3x faster than training without Tensor Cores while experiencing the benefits of mixed precision training.\'}', metadata={})]


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


 Recommend an API to identify the breed of a dog from a picture input.\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={})]


 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\': \'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={})]


 Create a 3D reconstruction of a scene from only one 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=\'MiDaS_small\', 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\', \'MiDaS_small\')", "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 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\': \'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={})]


 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\': \'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 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\': \'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 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\': \'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'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\': \'Image Recognition\', \'api_name\': \'vgg-nets\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'vgg16\', pretrained=True)", \'api_arguments\': [{\'name\': \'vgg16\', \'type\': \'str\', \'description\': \'VGG16 model\'}], \'python_environment_requirements\': [\'torch\', \'torchvision\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'vgg16\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'vgg16\': {\'Top-1 error\': 28.41, \'Top-5 error\': 9.62}}}, \'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={})]