 List an API that will allow me to input text that will be transformed into an audio file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Find a model that is optimal for the task of person re-identification from a set of images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


 I need an image classification model that can classify objects in images with high accuracy. Suggest me an API.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Help me find a way to classify different species of birds given an image from the Internet.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Your pet store is building a new image classifier for the different types of pets. Tell me which API can identify the breeds given pet images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'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 want to recognize objects in an image. Can you find me an API that can do this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'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 a photographer and I need to classify images according to their category. Write the code to use a machine learning API to achieve that.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'AlexNet\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'alexnet\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': {\'torch\': \'>=1.9.0\', \'torchvision\': \'>=0.10.0\'}, \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision:v0.10.0\', \'alexnet\', pretrained=True)", \'model.eval()\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 43.45, \'top-5_error\': 20.91}}, \'description\': \'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\'}', metadata={})]


 I want to classify images accurately without latency. Help me find an API to do that.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


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


 I need a Python library for calculating relative depth from a single image. What do you suggest?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Computing relative depth from a single image\', \'api_name\': \'MiDaS\', \'api_call\': "torch.hub.load(repo_or_dir=\'intel-isl/MiDaS\', model=\'DPT_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={})]


 Tell me an API that I can use to classify images into different categories using a pre-trained ResNet50 model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'ResNet50\', \'api_call\': "torch.hub.load(repo_or_dir=\'NVIDIA/DeepLearningExamples:torchhub\', model=\'nvidia_resnet50\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'pip install validators matplotlib\'], \'example_code\': [\'import torch\', \'from PIL import Image\', \'import torchvision.transforms as transforms\', \'import numpy as np\', \'import json\', \'import requests\', \'import matplotlib.pyplot as plt\', \'import warnings\', "warnings.filterwarnings(\'ignore\')", \'%matplotlib inline\', "device = torch.device(\'cuda\') if torch.cuda.is_available() else torch.device(\'cpu\')", "print(f\'Using {device} for inference\')", "resnet50 = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_resnet50\', pretrained=True)", "utils = torch.hub.load(\'NVIDIA/DeepLearningExamples:torchhub\', \'nvidia_convnets_processing_utils\')", \'resnet50.eval().to(device)\', \'uris = [...]\', \'batch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\', \'with torch.no_grad():\', \' output = torch.nn.functional.softmax(resnet50(batch), dim=1)\', \'results = utils.pick_n_best(predictions=output, n=5)\', \'for uri, result in zip(uris, results):\', \' img = Image.open(requests.get(uri, stream=True).raw)\', \' img.thumbnail((256,256), Image.ANTIALIAS)\', \' plt.imshow(img)\', \' plt.show()\', \' print(result)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'~0.5% top1 improvement over ResNet50 v1\'}, \'description\': \'The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model. The difference between v1 and v1.5 is that, in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec). The model is initialized as described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures.\'}', metadata={})]


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


 I need to analyze aerial images of agricultural fields to identify specific crop types. Can you suggest an API for classifying the crops in the images?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


 Find an API that allows me to classify pictures of animals with high accuracy.\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={})]


 An AI engineer is searching for an API capable of image classification. Please provide an SDK that uses a pre-trained model for image recognition tasks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


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


 Find the species of an animal in a given photo using an API.\n###Input: \"zebra.jpg\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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 on different edge devices with various resource constraints. Suggest an API suitable for this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Provide an API for converting text to speech, since the marketing team needs to generate realistic voices for a series of advertisements.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 I need an API that helps classify images with the highest accuracy. Tell me an API that can achieve this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Pinterest wants to build a system that can categorize images uploaded by users. What API should they use for this task?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image 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={})]


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


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


 A mobile application needs a machine learning model for object classification from various user images. Suggest an appropriate API for this task. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 I have a dataset with labeled images of clothing items from several fashion brands, and I want to classify them by brand. Which API can help me perform a classification task?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 I want to classify some images using a state-of-the-art model. Can you provide me an API to help in this task?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Show me an API that can efficiently classify images on mobile platforms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'MobileNet v2\', \'api_call\': "torch.hub.load(repo_or_dir=\'pytorch/vision\', model=\'mobilenet_v2\', pretrained=True)", \'api_arguments\': {\'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'PIL\', \'urllib\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'pytorch/vision\', \'mobilenet_v2\', pretrained=True)", \'model.eval()\', \'from PIL import Image\', \'from torchvision import transforms\', \'input_image = Image.open(filename)\', \'preprocess = transforms.Compose([\', \' transforms.Resize(256),\', \' transforms.CenterCrop(224),\', \' transforms.ToTensor(),\', \' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\', \'])\', \'input_tensor = preprocess(input_image)\', \'input_batch = input_tensor.unsqueeze(0)\', \'if torch.cuda.is_available():\', " input_batch = input_batch.to(\'cuda\')", " model.to(\'cuda\')", \'with torch.no_grad():\', \' output = model(input_batch)\', \'probabilities = torch.nn.functional.softmax(output[0], dim=0)\'], \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': {\'top-1_error\': 28.12, \'top-5_error\': 9.71}}, \'description\': \'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\'}', metadata={})]


 We are developing an app that can guess the type of a picture. We need it to work on most platforms with almost the same efficiency. Give me an API that can do it.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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 develop a photo sharing app like Instagram. Recommend an API to recognize objects in the photos uploaded by users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Google Photos wants to create a way to classify images uploaded by users into different categories. Recommend an API for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


 I have an image with animals in it; I need to know the species. Can you suggest an image recognition API that can identify the species of animals in the given image?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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 want to create an AI tool that automates recognizing objects in an image. Recommend an API that can do this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


 Is there any API that can identify plants from an image I provide?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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 mobile app developer needs an image classification API that can be used on a range of mobile devices without the need to adjust the model size. Recommend an API that fits this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Image Classification\', \'api_name\': \'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={})]


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


 I want to create a 3D visualization of a room using only a single image. How can I estimate the depth of the objects in the room from that image?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Computing relative depth from a single image\', \'api_name\': \'MiDaS\', \'api_call\': "torch.hub.load(repo_or_dir=\'intel-isl/MiDaS\', model=\'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={})]


 Give me an API that can predict the category of an object given its image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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 provide a GAN API that can generate high-quality 64x64 images for an apparel ecommerce company?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'Generative Adversarial Networks (GANs)\', \'api_name\': \'PGAN\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorch_GAN_zoo:hub\', model=\'PGAN\', pretrained=True)", \'api_arguments\': {\'repo_or_dir\': \'facebookresearch/pytorch_GAN_zoo:hub\', \'model\': \'PGAN\', \'model_name\': \'celebAHQ-512\', \'pretrained\': \'True\', \'useGPU\': \'use_gpu\'}, \'python_environment_requirements\': \'Python 3\', \'example_code\': {\'import\': \'import torch\', \'use_gpu\': \'use_gpu = True if torch.cuda.is_available() else False\', \'load_model\': "model = torch.hub.load(\'facebookresearch/pytorch_GAN_zoo:hub\', \'PGAN\', model_name=\'celebAHQ-512\', pretrained=True, useGPU=use_gpu)", \'build_noise_data\': \'noise, _ = model.buildNoiseData(num_images)\', \'test\': \'generated_images = model.test(noise)\', \'plot_images\': {\'import_matplotlib\': \'import matplotlib.pyplot as plt\', \'import_torchvision\': \'import torchvision\', \'make_grid\': \'grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)\', \'imshow\': \'plt.imshow(grid.permute(1, 2, 0).cpu().numpy())\', \'show\': \'plt.show()\'}}, \'performance\': {\'dataset\': \'celebA\', \'accuracy\': \'High-quality celebrity faces\'}, \'description\': "Progressive Growing of GANs (PGAN) is a method for generating high-resolution images using generative adversarial networks. The model is trained progressively, starting with low-resolution images and gradually increasing the resolution until the desired output is achieved. This implementation is based on the paper by Tero Karras et al., \'Progressive Growing of GANs for Improved Quality, Stability, and Variation\'."}', metadata={})]


 I am a city planner responsible for managing different areas of the city. Recommend an API that can segment roads, parks and buildings from a satellite image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 I am starting a startup that recommends clothing to users based on images of their outfits. What is a good API for this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Classification\', \'framework\': \'PyTorch\', \'functionality\': \'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={})]


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


 I need an efficient AI-based classifier to identify products on grocery store shelves. Suggest an appropriate API to implement this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 A digital artist needs an API that can recognize and classify images containing multiple objects. Which API would you suggest?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 I am a developer at Audible and I am looking for an API that can convert text to speech, find something suitable.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


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


 Can you provide me an API for classifying a video content based on the actions performed in it?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video Classification\', \'framework\': \'PyTorchVideo\', \'functionality\': \'SlowFast Networks\', \'api_name\': \'torch.hub.load\', \'api_call\': "torch.hub.load(repo_or_dir=\'facebookresearch/pytorchvideo\', model=\'slowfast_r50\', pretrained=True)", \'api_arguments\': {\'repository\': \'facebookresearch/pytorchvideo\', \'model\': \'slowfast_r50\', \'pretrained\': \'True\'}, \'python_environment_requirements\': [\'torch\', \'torchvision\', \'pytorchvideo\'], \'example_code\': [\'import torch\', "model = torch.hub.load(\'facebookresearch/pytorchvideo\', \'slowfast_r50\', pretrained=True)", "device = \'cpu\'", \'model = model.eval()\', \'model = model.to(device)\'], \'performance\': {\'dataset\': \'Kinetics 400\', \'accuracy\': {\'top1\': 76.94, \'top5\': 92.69}, \'flops\': 65.71, \'params\': 34.57}, \'description\': "Slow Fast model architectures are based on the paper \'SlowFast Networks for Video Recognition\' by Christoph Feichtenhofer et al. They are pretrained on the Kinetics 400 dataset using the 8x8 setting. This model is capable of classifying video clips into different action categories. It is provided by the FAIR PyTorchVideo library."}', metadata={})]


 A startup called \"DriveMe\" is building a vehicular safety app and wants to detect traffic objects, segment drivable areas, and detect lanes in real-time. Suggest an API to help them achieve their goal.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Object Detection\', \'framework\': \'PyTorch\', \'functionality\': \'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\', \'api_name\': \'HybridNets\', \'api_call\': "torch.hub.load(repo_or_dir=\'datvuthanh/hybridnets\', model=\'hybridnets\', pretrained=True)", \'api_arguments\': \'pretrained\', \'python_environment_requirements\': \'Python>=3.7, PyTorch>=1.10\', \'example_code\': "import torch\\nmodel = torch.hub.load(\'datvuthanh/hybridnets\', \'hybridnets\', pretrained=True)\\nimg = torch.randn(1,3,640,384)\\nfeatures, regression, classification, anchors, segmentation = model(img)", \'performance\': {\'dataset\': [{\'name\': \'BDD100K\', \'accuracy\': {\'Traffic Object Detection\': {\'Recall (%)\': 92.8, \'mAP@0.5 (%)\': 77.3}, \'Drivable Area Segmentation\': {\'Drivable mIoU (%)\': 90.5}, \'Lane Line Detection\': {\'Accuracy (%)\': 85.4, \'Lane Line IoU (%)\': 31.6}}}]}, \'description\': \'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\'}', metadata={})]