 A city is planning to survey the land for urban development. Provide me with an API that can identify buildings and roads from an aerial photo.\n
 Use this API documentation for reference:  {"domain": "Semantic Segmentation", "framework": "PyTorch", "functionality": "Fully-Convolutional Network", "api_name": "fcn_resnet101", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='fcn_resnet101', pretrained=True)", "api_arguments": [{"name": "pretrained", "type": "boolean", "description": "If True, returns a model pre-trained on COCO train2017"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "matplotlib"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'fcn_resnet101', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "input_image = input_image.convert(\"RGB\")", "preprocess = transforms.Compose([", " 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)['out'][0]", "output_predictions = output.argmax(0)"], "performance": {"dataset": "COCO val2017", "accuracy": {"fcn_resnet101": {"Mean IOU": 63.7, "Global Pixelwise Accuracy": 91.9}}}, "description": "FCN-ResNet is a Fully-Convolutional Network model using a ResNet-50 or a ResNet-101 backbone. The pre-trained models have been trained on a subset of COCO train2017, on the 20 categories that are present in the Pascal VOC dataset."}}


 I need an efficient model for classifying animals in images taken by wildlife cameras. Suggest me an API for this purpose.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "HarDNet", "api_call": "torch.hub.load(repo_or_dir='PingoLH/Pytorch-HarDNet', model='hardnet39ds', pretrained=True)", "api_arguments": [{"name": "hardnet39ds", "type": "str", "description": "HarDNet-39DS model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"hardnet39ds": {"Top-1 error": 27.92, "Top-5 error": 9.57}}}, "description": "Harmonic DenseNet (HarDNet) is a low memory traffic CNN model, which is fast and efficient. The basic concept is to minimize both computational cost and memory access cost at the same time, such that the HarDNet models are 35% faster than ResNet running on GPU comparing to models with the same accuracy (except the two DS models that were designed for comparing with MobileNet)."}}


 The company is creating a neural network model that can run efficiently on different hardware platforms. Tell me an API that specializes CNNs for different hardware.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "ProxylessNAS", "api_name": "mit-han-lab/ProxylessNAS", "api_call": "torch.hub.load(repo_or_dir='mit-han-lab/ProxylessNAS', model='proxylessnas_cpu', pretrained=True)", "api_arguments": [{"name": "proxylessnas_cpu", "type": "str", "description": "ProxylessNAS optimized for CPU"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "target_platform = 'proxyless_cpu'", "model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)", "model.eval()"], "performance": {"dataset": [{"model_structure": "proxylessnas_cpu", "accuracy": 75.3}]}, "description": "ProxylessNAS models are from the ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware paper. They specialize CNN architectures for different hardware platforms, offering free yet significant performance boost on all three platforms (CPU, GPU, and Mobile) with similar accuracy."}}


 Farlando Corp has an application that runs on their customers' GPUs, and they want a neural network that is optimized on GPU performance. Recommend an API that they can use for image classification.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "ProxylessNAS", "api_name": "mit-han-lab/ProxylessNAS", "api_call": "torch.hub.load(repo_or_dir='mit-han-lab/ProxylessNAS', model='proxylessnas_gpu', pretrained=True)", "api_arguments": [{"name": "proxylessnas_gpu", "type": "str", "description": "ProxylessNAS optimized for GPU"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "target_platform = 'proxyless_gpu'", "model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)", "model.eval()"], "performance": {"dataset": [{"model_structure": "proxylessnas_gpu", "accuracy": 75.1}]}, "description": "ProxylessNAS models are from the ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware paper. They specialize CNN architectures for different hardware platforms, offering free yet significant performance boost on all three platforms (CPU, GPU, and Mobile) with similar accuracy."}}


 I need an efficient model for image classification with good accuracy. Provide me with an API that uses LIF neurons.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SNNMLP", "api_call": "torch.hub.load(repo_or_dir='huawei-noah/Efficient-AI-Backbones', model='snnmlp_s', pretrained=True)", "api_arguments": [{"name": "snnmlp_s", "type": "str", "description": "SNNMLP Small model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('huawei-noah/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "SNNMLP Small", "top-1": 83.3}}, "description": "SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively."}}


 As a market research analyst, I want to find a tool to classify different product types using their images.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Recognition", "api_name": "vgg-nets", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='vgg11', pretrained=True)", "api_arguments": [{"name": "vgg11", "type": "str", "description": "VGG11 model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'vgg11', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"vgg11": {"Top-1 error": 30.98, "Top-5 error": 11.37}}}, "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."}}


 A media company that works with image recognition is trying to identify an object in an image. Recommend an API that specializes in image recognition.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Recognition", "api_name": "vgg-nets", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='vgg11_bn', pretrained=True)", "api_arguments": [{"name": "vgg11_bn", "type": "str", "description": "VGG11 model with batch normalization"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'vgg11_bn', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"vgg11_bn": {"Top-1 error": 26.7, "Top-5 error": 8.58}}}, "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."}}


 Inform me of an API that can help identify famous landmarks from images.\n
 Use this API documentation for reference:  {"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."}}


 I am working on an image classification project where accuracy is important, and I need a pretrained model that has a lower error rate when classifying images. What model might work for me?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Wide Residual Networks", "api_name": "wide_resnet50_2", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='wide_resnet50_2', pretrained=True)", "api_arguments": "pretrained", "python_environment_requirements": "torch, torchvision", "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet50_2', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"wide_resnet50_2": {"Top-1 error": 21.49, "Top-5 error": 5.91}}}, "description": "Wide Residual networks simply have increased number of channels compared to ResNet. Otherwise the architecture is the same. Deeper ImageNet models with bottleneck block have increased number of channels in the inner 3x3 convolution. The wide_resnet50_2 and wide_resnet101_2 models were trained in FP16 with mixed precision training using SGD with warm restarts. Checkpoints have weights in half precision (except batch norm) for smaller size, and can be used in FP32 models too."}}


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


 Recommend a pretrained API that classifies animals from an image given the photo of the animal.\n
 Use this API documentation for reference:  {"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\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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."}}


 I have a picture of my dog and I want to classify its breed. Provide me an API to do this.\n
 Use this API documentation for reference:  {"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_075', pretrained=True)", "api_arguments": {"model_name": "mealv2_mobilenetv3_small_075"}, "python_environment_requirements": "!pip install timm", "example_code": "import torch\nfrom PIL import Image\nfrom torchvision import transforms\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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 0.75", "resolution": "224", "parameters": "2.04M", "top1": "67.60", "top5": "87.23"}}], "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."}}


 A developer at Pinterest wants to automatically categorize uploaded images based on their content. Provide an API suggestion that can help with this task.\n
 Use this API documentation for reference:  {"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\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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."}}


 A startup is working on a computer vision application supporting autonomous drones. Can you provide an API that can compute the relative depth of an object in a given image?\n
 Use this API documentation for reference:  {"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."}}


 Imagine you are trying to build podcast transcription for people who are impaired. Get an API to transcribe a sample podcast from Spotify.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Speech-To-Text", "api_name": "Silero Models", "api_call": "torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', pretrained=True)", "api_arguments": {"repo_or_dir": "snakers4/silero-models", "model": "silero_stt", "language": ["en", "de", "es"], "device": "device"}, "python_environment_requirements": ["pip install -q torchaudio omegaconf soundfile"], "example_code": ["import torch", "import zipfile", "import torchaudio", "from glob import glob", "device = torch.device('cpu')", "model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', language='en', device=device)", "(read_batch, split_into_batches, read_audio, prepare_model_input) = utils", "torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav', dst ='speech_orig.wav', progress=True)", "test_files = glob('speech_orig.wav')", "batches = split_into_batches(test_files, batch_size=10)", "input = prepare_model_input(read_batch(batches[0]), device=device)", "output = model(input)", "for example in output:", " print(decoder(example.cpu()))"], "performance": {"dataset": "Open-STT", "accuracy": "See the wiki for quality and performance benchmarks"}, "description": "Silero Speech-To-Text models provide enterprise grade STT in a compact form-factor for several commonly spoken languages. The models are robust to a variety of dialects, codecs, domains, noises, and lower sampling rates. They consume a normalized audio in the form of samples and output frames with token probabilities. A decoder utility is provided for simplicity."}}


 A tourist is planning to take a picture of a beautiful scene but wants to separate the people from the background. Recommend an API to help do this.\n
 Use this API documentation for reference:  {"domain": "Semantic Segmentation", "framework": "PyTorch", "functionality": "DeepLabV3", "api_name": "torch.hub.load", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='deeplabv3_resnet101', pretrained=True)", "api_arguments": {"pretrained": "True"}, "python_environment_requirements": {"torch": "latest", "torchvision": "latest", "PIL": "latest", "matplotlib": "latest"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet101', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "input_image = input_image.convert('RGB')", "preprocess = transforms.Compose([", " 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)['out'][0]", "output_predictions = output.argmax(0)"], "performance": {"dataset": "COCO val2017", "accuracy": [{"model": "deeplabv3_resnet101", "Mean IOU": 67.4, "Global Pixelwise Accuracy": 92.4}]}, "description": "DeepLabV3 models with ResNet-50, ResNet-101 and MobileNet-V3 backbones for semantic segmentation. The pre-trained models have been trained on a subset of COCO train2017, on the 20 categories that are present in the Pascal VOC dataset."}}


 I took a photo and I want to detect all the objects in the image. Provide me with an API to do this.\n
 Use this API documentation for reference:  {"domain": "Object Detection", "framework": "PyTorch", "functionality": "YOLOv5", "api_name": "torch.hub.load", "api_call": "torch.hub.load(repo_or_dir='ultralytics/yolov5', model='yolov5s', pretrained=True)", "api_arguments": ["'ultralytics/yolov5'", "'yolov5s'", "pretrained=True"], "python_environment_requirements": "Python>=3.8, PyTorch>=1.7", "example_code": ["import torch", "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)", "imgs = ['https://ultralytics.com/images/zidane.jpg']", "results = model(imgs)", "results.print()", "results.save()", "results.xyxy[0]", "results.pandas().xyxy[0]"], "performance": {"dataset": "COCO", "accuracy": {"YOLOv5s6": {"mAPval0.5:0.95": 43.3, "mAPtest0.5:0.95": 43.3, "mAPval0.5": 61.9}, "YOLOv5m6": {"mAPval0.5:0.95": 50.5, "mAPtest0.5:0.95": 50.5, "mAPval0.5": 68.7}, "YOLOv5l6": {"mAPval0.5:0.95": 53.4, "mAPtest0.5:0.95": 53.4, "mAPval0.5": 71.1}, "YOLOv5x6": {"mAPval0.5:0.95": 54.4, "mAPtest0.5:0.95": 54.4, "mAPval0.5": 72.0}, "YOLOv5x6 TTA": {"mAPval0.5:0.95": 55.0, "mAPtest0.5:0.95": 55.0, "mAPval0.5": 72.0}}}, "description": "YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite."}}


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


 I am trying to classify an image to find its category. Please give me an API that can identify the content of an image.\n
 Use this API documentation for reference:  {"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."}}


 I would like to convert text to natural sounding speech using Deep Learning. Can you provide me with an API to achieve this?\n
 Use this API documentation for reference:  {"domain": "Text-to-Speech", "framework": "PyTorch", "functionality": "Speech Synthesis", "api_name": "WaveGlow", "api_call": "torch.hub.load(repo_or_dir='NVIDIA/DeepLearningExamples:torchhub', model='nvidia_waveglow', pretrained=True)", "api_arguments": {"repo_or_dir": "NVIDIA/DeepLearningExamples:torchhub", "model": "nvidia_waveglow", "model_math": "fp32"}, "python_environment_requirements": ["numpy", "scipy", "librosa", "unidecode", "inflect", "libsndfile1"], "example_code": {"load_waveglow_model": "waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')", "prepare_waveglow_model": ["waveglow = waveglow.remove_weightnorm(waveglow)", "waveglow = waveglow.to('cuda')", "waveglow.eval()"], "load_tacotron2_model": "tacotron2 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp32')", "prepare_tacotron2_model": ["tacotron2 = tacotron2.to('cuda')", "tacotron2.eval()"], "synthesize_speech": ["text = \"hello world, I missed you so much\"", "utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tts_utils')", "sequences, lengths = utils.prepare_input_sequence([text])", "with torch.no_grad():", " mel, _, _ = tacotron2.infer(sequences, lengths)", " audio = waveglow.infer(mel)", "audio_numpy = audio[0].data.cpu().numpy()", "rate = 22050"], "save_audio": "write(\"audio.wav\", rate, audio_numpy)", "play_audio": "Audio(audio_numpy, rate=rate)"}, "performance": {"dataset": "LJ Speech", "accuracy": null}, "description": "The Tacotron 2 and WaveGlow model form a text-to-speech system that enables users to synthesize natural-sounding speech from raw transcripts without any additional prosody information. The Tacotron 2 model produces mel spectrograms from input text using encoder-decoder architecture. WaveGlow is a flow-based model that consumes the mel spectrograms to generate speech."}}


 Design a system to diagnose diseases from X-Ray images. Recommend an appropriate API for classifying diseases in the X-Ray images.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Dense Convolutional Network", "api_name": "Densenet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='densenet169', pretrained=True)", "api_arguments": [{"name": "densenet169", "type": "str", "description": "Densenet-169 model"}], "python_environment_requirements": {"torch": "latest", "torchvision": "latest"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet169', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"densenet169": {"Top-1 error": 24.0, "Top-5 error": 7.0}}}, "description": "Dense Convolutional Network (DenseNet) connects each layer to every other layer in a feed-forward fashion. It alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and substantially reduces the number of parameters."}}


 A smartphone company is developing an app that can classify object from a picture. Provide an API that can achieve this task.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "HarDNet", "api_call": "torch.hub.load(repo_or_dir='PingoLH/Pytorch-HarDNet', model='hardnet39ds', pretrained=True)", "api_arguments": [{"name": "hardnet39ds", "type": "str", "description": "HarDNet-39DS model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"hardnet39ds": {"Top-1 error": 27.92, "Top-5 error": 9.57}}}, "description": "Harmonic DenseNet (HarDNet) is a low memory traffic CNN model, which is fast and efficient. The basic concept is to minimize both computational cost and memory access cost at the same time, such that the HarDNet models are 35% faster than ResNet running on GPU comparing to models with the same accuracy (except the two DS models that were designed for comparing with MobileNet)."}}


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


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


 Find out an API that can identify 102 different types of flowers from an image.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "ProxylessNAS", "api_name": "mit-han-lab/ProxylessNAS", "api_call": "torch.hub.load(repo_or_dir='mit-han-lab/ProxylessNAS', model='proxylessnas_mobile', pretrained=True)", "api_arguments": [{"name": "proxylessnas_mobile", "type": "str", "description": "ProxylessNAS optimized for Mobile"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "target_platform = 'proxyless_mobile'", "model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)", "model.eval()"], "performance": {"dataset": [{"model_structure": "proxylessnas_mobile", "accuracy": 74.6}]}, "description": "ProxylessNAS models are from the ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware paper. They specialize CNN architectures for different hardware platforms, offering free yet significant performance boost on all three platforms (CPU, GPU, and Mobile) with similar accuracy."}}


 Can you recommend an API for image classification which is efficient in terms of computational resources and has decent accuracy?\n
 Use this API documentation for reference:  {"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."}}


 A photography service needs a fast algorithm to recognize objects in their images from the ImageNet dataset out of the box. What API should they use?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Deep Residual Networks", "api_name": "ResNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnet18', pretrained=True)", "api_arguments": [{"name": "pretrained", "type": "bool", "default": "False", "description": "If True, returns a model pre-trained on ImageNet"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "resnet18", "top-1_error": 30.24, "top-5_error": 10.92}}, "description": "ResNet models are deep residual networks pre-trained on ImageNet. They were proposed in the paper 'Deep Residual Learning for Image Recognition'. Available model variants include ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152."}}


 Can you suggest an API for classifying images in my dataset using a model with spiking neural networks?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SNNMLP", "api_call": "torch.hub.load(repo_or_dir='huawei-noah/Efficient-AI-Backbones', model='snnmlp_t', pretrained=True)", "api_arguments": [{"name": "snnmlp_t", "type": "str", "description": "SNNMLP Tiny model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('huawei-noah/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "SNNMLP Tiny", "top-1": 81.88}}, "description": "SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively."}}


 I am trying to recognize objects in an image using a popular image classification model. Which model should I use?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Recognition", "api_name": "vgg-nets", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='vgg11', pretrained=True)", "api_arguments": [{"name": "vgg11", "type": "str", "description": "VGG11 model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'vgg11', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"vgg11": {"Top-1 error": 30.98, "Top-5 error": 11.37}}}, "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."}}


 I want to create an app to recognize objects in images. Which API is suitable for this task?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Recognition", "api_name": "vgg-nets", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='vgg19', pretrained=True)", "api_arguments": [{"name": "vgg19", "type": "str", "description": "VGG19 model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'vgg19', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"vgg19": {"Top-1 error": 27.62, "Top-5 error": 9.12}}}, "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."}}


 Air Traffic Control needs an image classifier to identify if an image contains an aircraft or not. Suggest an API that would be suitable for this task.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Recognition", "api_name": "vgg-nets", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='vgg19_bn', pretrained=True)", "api_arguments": [{"name": "vgg19_bn", "type": "str", "description": "VGG19 model with batch normalization"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'vgg19_bn', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"vgg19_bn": {"Top-1 error": 25.76, "Top-5 error": 8.15}}}, "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."}}


 A smart fridge wants to identify food items from images taken from its camera. Provide an API to identify the food items.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Wide Residual Networks", "api_name": "wide_resnet50_2", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='wide_resnet50_2', pretrained=True)", "api_arguments": "pretrained", "python_environment_requirements": "torch, torchvision", "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet50_2', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"wide_resnet50_2": {"Top-1 error": 21.49, "Top-5 error": 5.91}}}, "description": "Wide Residual networks simply have increased number of channels compared to ResNet. Otherwise the architecture is the same. Deeper ImageNet models with bottleneck block have increased number of channels in the inner 3x3 convolution. The wide_resnet50_2 and wide_resnet101_2 models were trained in FP16 with mixed precision training using SGD with warm restarts. Checkpoints have weights in half precision (except batch norm) for smaller size, and can be used in FP32 models too."}}


 I want to count how many people are present in a room using an image. Tell me an API that can do this task.\n
 Use this API documentation for reference:  {"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\nssd_model = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd')\nutils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd_processing_utils')\nssd_model.to('cuda')\nssd_model.eval()\ninputs = [utils.prepare_input(uri) for uri in uris]\ntensor = utils.prepare_tensor(inputs)\nwith torch.no_grad():\n detections_batch = ssd_model(tensor)\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."}}


 I am developing a website that can predict the content of an image based on its URL. What API would you recommend with a code example?\n###Input: {\"image_url\": \"https://example.com/image.jpg\"}\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "EfficientNet", "api_call": "torch.hub.load(repo_or_dir='NVIDIA/DeepLearningExamples:torchhub', model='nvidia_efficientnet_b0', pretrained=True)", "api_arguments": ["model_name", "pretrained"], "python_environment_requirements": ["validators", "matplotlib"], "example_code": "import torch\nefficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\nutils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')\nefficientnet.eval().to(device)\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(efficientnet(batch), dim=1)\n \nresults = utils.pick_n_best(predictions=output, n=5)", "performance": {"dataset": {"name": "IMAGENET", "accuracy": "Not provided"}}, "description": "EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy."}}


 A wildlife photographer wants to classify animals in images taken during a safari. Provide me with an API that can help classify these animals.\n
 Use this API documentation for reference:  {"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_380x380', pretrained=True)", "api_arguments": {"model_name": "mealv2_resnest50_380x380"}, "python_environment_requirements": "!pip install timm", "example_code": "import torch\nfrom PIL import Image\nfrom torchvision import transforms\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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": "380", "parameters": "25.6M", "top1": "81.72", "top5": "95.81"}}], "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."}}


 I want to use my camera app to identify objects that I point it to. What API would you recommend?\n
 Use this API documentation for reference:  {"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\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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."}}


 I am building an image classification model and want to achieve a high accuracy. Which API should I use?\n
 Use this API documentation for reference:  {"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_mobilenet_v3_large_100', pretrained=True)", "api_arguments": {"model_name": "mealv2_mobilenet_v3_large_100"}, "python_environment_requirements": "!pip install timm", "example_code": "import torch\nfrom PIL import Image\nfrom torchvision import transforms\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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-Large 1.0", "resolution": "224", "parameters": "5.48M", "top1": "76.92", "top5": "93.32"}}], "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."}}


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


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


 Provide an API recommendation for a call center which wants to convert customer voice calls into text.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Speech-To-Text", "api_name": "Silero Models", "api_call": "torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', pretrained=True)", "api_arguments": {"repo_or_dir": "snakers4/silero-models", "model": "silero_stt", "language": ["en", "de", "es"], "device": "device"}, "python_environment_requirements": ["pip install -q torchaudio omegaconf soundfile"], "example_code": ["import torch", "import zipfile", "import torchaudio", "from glob import glob", "device = torch.device('cpu')", "model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', language='en', device=device)", "(read_batch, split_into_batches, read_audio, prepare_model_input) = utils", "torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav', dst ='speech_orig.wav', progress=True)", "test_files = glob('speech_orig.wav')", "batches = split_into_batches(test_files, batch_size=10)", "input = prepare_model_input(read_batch(batches[0]), device=device)", "output = model(input)", "for example in output:", " print(decoder(example.cpu()))"], "performance": {"dataset": "Open-STT", "accuracy": "See the wiki for quality and performance benchmarks"}, "description": "Silero Speech-To-Text models provide enterprise grade STT in a compact form-factor for several commonly spoken languages. The models are robust to a variety of dialects, codecs, domains, noises, and lower sampling rates. They consume a normalized audio in the form of samples and output frames with token probabilities. A decoder utility is provided for simplicity."}}


 Provide me with an API that can tackle city-scape segmentation in autonomous driving application.\n
 Use this API documentation for reference:  {"domain": "Semantic Segmentation", "framework": "PyTorch", "functionality": "DeepLabV3", "api_name": "torch.hub.load", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='deeplabv3_resnet101', pretrained=True)", "api_arguments": {"pretrained": "True"}, "python_environment_requirements": {"torch": "latest", "torchvision": "latest", "PIL": "latest", "matplotlib": "latest"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet101', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "input_image = input_image.convert('RGB')", "preprocess = transforms.Compose([", " 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)['out'][0]", "output_predictions = output.argmax(0)"], "performance": {"dataset": "COCO val2017", "accuracy": [{"model": "deeplabv3_resnet101", "Mean IOU": 67.4, "Global Pixelwise Accuracy": 92.4}]}, "description": "DeepLabV3 models with ResNet-50, ResNet-101 and MobileNet-V3 backbones for semantic segmentation. The pre-trained models have been trained on a subset of COCO train2017, on the 20 categories that are present in the Pascal VOC dataset."}}


 I need an API to extract features from a collection of photographs taken at the 2022 Olympics.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "ResNext WSL", "api_call": "torch.hub.load(repo_or_dir='facebookresearch/WSL-Images', model='resnext101_32x8d_wsl', pretrained=True)", "api_arguments": [{"name": "resnext101_32x8d_wsl", "type": "str", "description": "ResNeXt-101 32x8d WSL model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x8d_wsl')", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(output[0])", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"ResNeXt-101 32x8d": {"Top-1 Acc.": "82.2", "Top-5 Acc.": "96.4"}}}, "description": "The provided ResNeXt models are pre-trained in weakly-supervised fashion on 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. The models significantly improve the training accuracy on ImageNet compared to training from scratch. They achieve state-of-the-art accuracy of 85.4% on ImageNet with the ResNext-101 32x48d model."}}


 An E-commerce manager wants to develop an image classification system for their products. They need a powerful pre-trained model as a starting point. Recommend an API for this purpose.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "ResNext WSL", "api_call": "torch.hub.load(repo_or_dir='facebookresearch/WSL-Images', model='resnext101_32x32d_wsl', pretrained=True)", "api_arguments": [{"name": "resnext101_32x32d_wsl", "type": "str", "description": "ResNeXt-101 32x32d WSL model"}], "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x32d_wsl')", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(output[0])", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"ResNeXt-101 32x32d": {"Top-1 Acc.": "85.1", "Top-5 Acc.": "97.5"}}}, "description": "The provided ResNeXt models are pre-trained in weakly-supervised fashion on 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. The models significantly improve the training accuracy on ImageNet compared to training from scratch. They achieve state-of-the-art accuracy of 85.4% on ImageNet with the ResNext-101 32x48d model."}}


 I need an API to classify images with known objects. Suggest a suitable model that can do this.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Dense Convolutional Network", "api_name": "Densenet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='densenet169', pretrained=True)", "api_arguments": [{"name": "densenet169", "type": "str", "description": "Densenet-169 model"}], "python_environment_requirements": {"torch": "latest", "torchvision": "latest"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet169', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"densenet169": {"Top-1 error": 24.0, "Top-5 error": 7.0}}}, "description": "Dense Convolutional Network (DenseNet) connects each layer to every other layer in a feed-forward fashion. It alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and substantially reduces the number of parameters."}}


 A delivery company wants to recognize if a package is damaged during shipment. Propose an API that can classify images into damaged and undamaged packages.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "IBN-Net", "api_name": "torch.hub.load", "api_call": "torch.hub.load(repo_or_dir='XingangPan/IBN-Net', model='resnet101_ibn_a', pretrained=True)", "api_arguments": [{"name": "resnet101_ibn_a", "type": "str", "description": "ResNet-101-IBN-a model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('XingangPan/IBN-Net', 'resnet101_ibn_a', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"resnet101_ibn_a": {"Top-1 acc": 78.61, "Top-5 acc": 94.41}}}, "description": "IBN-Net is a CNN model with domain/appearance invariance. Motivated by style transfer works, IBN-Net carefully unifies instance normalization and batch normalization in a single deep network. It provides a simple way to increase both modeling and generalization capacities without adding model complexity. IBN-Net is especially suitable for cross domain or person/vehicle re-identification tasks."}}


 An image recognition app needs to identify objects from the images it captures. Suggest an API which is optimized for GPUs.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "ProxylessNAS", "api_name": "mit-han-lab/ProxylessNAS", "api_call": "torch.hub.load(repo_or_dir='mit-han-lab/ProxylessNAS', model='proxylessnas_gpu', pretrained=True)", "api_arguments": [{"name": "proxylessnas_gpu", "type": "str", "description": "ProxylessNAS optimized for GPU"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "target_platform = 'proxyless_gpu'", "model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)", "model.eval()"], "performance": {"dataset": [{"model_structure": "proxylessnas_gpu", "accuracy": 75.1}]}, "description": "ProxylessNAS models are from the ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware paper. They specialize CNN architectures for different hardware platforms, offering free yet significant performance boost on all three platforms (CPU, GPU, and Mobile) with similar accuracy."}}


 Show me an API that provides easy to use neural networks for classifying different types of wildlife on mobile platforms.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "ProxylessNAS", "api_name": "mit-han-lab/ProxylessNAS", "api_call": "torch.hub.load(repo_or_dir='mit-han-lab/ProxylessNAS', model='proxylessnas_mobile', pretrained=True)", "api_arguments": [{"name": "proxylessnas_mobile", "type": "str", "description": "ProxylessNAS optimized for Mobile"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "target_platform = 'proxyless_mobile'", "model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)", "model.eval()"], "performance": {"dataset": [{"model_structure": "proxylessnas_mobile", "accuracy": 74.6}]}, "description": "ProxylessNAS models are from the ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware paper. They specialize CNN architectures for different hardware platforms, offering free yet significant performance boost on all three platforms (CPU, GPU, and Mobile) with similar accuracy."}}


 Recommend an API for identifying defective parts in a manufacturing assembly line based on images taken by an inspection system.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Deep Residual Networks", "api_name": "ResNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnet18', pretrained=True)", "api_arguments": [{"name": "pretrained", "type": "bool", "default": "False", "description": "If True, returns a model pre-trained on ImageNet"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "resnet18", "top-1_error": 30.24, "top-5_error": 10.92}}, "description": "ResNet models are deep residual networks pre-trained on ImageNet. They were proposed in the paper 'Deep Residual Learning for Image Recognition'. Available model variants include ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152."}}


 Identify an image classification API that can be used to determine if an object is a car, a bike, or a pedestrian.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Deep Residual Networks", "api_name": "ResNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnet34', pretrained=True)", "api_arguments": [{"name": "pretrained", "type": "bool", "default": "False", "description": "If True, returns a model pre-trained on ImageNet"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet34', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "resnet34", "top-1_error": 26.7, "top-5_error": 8.58}}, "description": "ResNet models are deep residual networks pre-trained on ImageNet. They were proposed in the paper 'Deep Residual Learning for Image Recognition'. Available model variants include ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152."}}


 I need an API to classify images efficiently without sacrificing too much accuracy. Can you provide me with one?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SNNMLP", "api_call": "torch.hub.load(repo_or_dir='huawei-noah/Efficient-AI-Backbones', model='snnmlp_t', pretrained=True)", "api_arguments": [{"name": "snnmlp_t", "type": "str", "description": "SNNMLP Tiny model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('huawei-noah/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "SNNMLP Tiny", "top-1": 81.88}}, "description": "SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively."}}


 To save the environment, a student wants to evaluate how green his schools area is. Tell me an AI API which can classify the images of plants in his environment and tell the name of the plants.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Wide Residual Networks", "api_name": "wide_resnet101_2", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='wide_resnet101_2', pretrained=True)", "api_arguments": "pretrained", "python_environment_requirements": "torch, torchvision", "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet101_2', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"wide_resnet101_2": {"Top-1 error": 21.16, "Top-5 error": 5.72}}}, "description": "Wide Residual networks simply have increased number of channels compared to ResNet. Otherwise the architecture is the same. Deeper ImageNet models with bottleneck block have increased number of channels in the inner 3x3 convolution. The wide_resnet50_2 and wide_resnet101_2 models were trained in FP16 with mixed precision training using SGD with warm restarts. Checkpoints have weights in half precision (except batch norm) for smaller size, and can be used in FP32 models too."}}


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


 I want my app to be able to read aloud the text for audiobooks. Can you suggest me an API for converting text to speech?\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Text-to-Speech", "api_name": "Tacotron 2", "api_call": "torch.hub.load(repo_or_dir='NVIDIA/DeepLearningExamples:torchhub', model='nvidia_tacotron2', model_math='fp16')", "api_arguments": {"model_math": "fp16"}, "python_environment_requirements": ["numpy", "scipy", "librosa", "unidecode", "inflect", "libsndfile1"], "example_code": ["import torch", "tacotron2 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')", "tacotron2 = tacotron2.to('cuda')", "tacotron2.eval()", "text = 'Hello world, I missed you so much.'", "utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tts_utils')", "sequences, lengths = utils.prepare_input_sequence([text])", "with torch.no_grad():", " mel, _, _ = tacotron2.infer(sequences, lengths)", " audio = waveglow.infer(mel)", "audio_numpy = audio[0].data.cpu().numpy()", "rate = 22050"], "performance": {"dataset": "LJ Speech", "accuracy": "Not specified"}, "description": "The Tacotron 2 model generates mel spectrograms from input text using an encoder-decoder architecture, and it is designed for generating natural-sounding speech from raw transcripts without any additional prosody information. This implementation uses Dropout instead of Zoneout to regularize the LSTM layers. The WaveGlow model (also available via torch.hub) is a flow-based model that consumes the mel spectrograms to generate speech."}}


 An app wants to identify dog breeds from images taken by users. Recommend an API that can classify the dog breed given a photo of a dog.\n
 Use this API documentation for reference:  {"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\nmodel = torch.hub.load('szq0214/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\nmodel.eval()\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)\nif torch.cuda.is_available():\n input_batch = input_batch.to('cuda')\n model.to('cuda')\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."}