 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:  {"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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "IBN-Net", "api_name": "torch.hub.load", "api_call": "torch.hub.load(repo_or_dir='XingangPan/IBN-Net', model='resnet50_ibn_a', pretrained=True)", "api_arguments": [{"name": "resnet50_ibn_a", "type": "str", "description": "ResNet-50-IBN-a model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('XingangPan/IBN-Net', 'resnet50_ibn_a', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"resnet50_ibn_a": {"Top-1 acc": 77.46, "Top-5 acc": 93.68}}}, "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."}}


 Query an API that carries out vehicle or person re-identification tasks accurately.\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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Deep Residual Networks", "api_name": "ResNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnet101', 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', 'resnet101', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "resnet101", "top-1_error": 22.63, "top-5_error": 6.44}}, "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."}}


 Help me find a way to classify different species of birds given an image from the Internet.\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_b', pretrained=True)", "api_arguments": [{"name": "snnmlp_b", "type": "str", "description": "SNNMLP Base model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('huawei-noah/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "SNNMLP Base", "top-1": 85.59}}, "description": "SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively."}}


 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:  {"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 recognize objects in an image. Can you find me an API that can do this?\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."}}


 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:  {"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 classify images accurately without latency. Help me find an API to do that.\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."}}


 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:  {"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."}}


 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:  {"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."}}


 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:  {"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."}}


 I need a Python library for calculating relative depth from a single image. What do you suggest?\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."}}


 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:  {"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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Dense Convolutional Network", "api_name": "Densenet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='densenet121', pretrained=True)", "api_arguments": [{"name": "densenet121", "type": "str", "description": "Densenet-121 model"}], "python_environment_requirements": {"torch": "latest", "torchvision": "latest"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet121', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"densenet121": {"Top-1 error": 25.35, "Top-5 error": 7.83}}}, "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."}}


 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:  {"domain": "Semantic Segmentation", "framework": "PyTorch", "functionality": "Fully-Convolutional Network", "api_name": "fcn_resnet50", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='fcn_resnet50', 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_resnet50', 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_resnet50": {"Mean IOU": 60.5, "Global Pixelwise Accuracy": 91.4}}}, "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."}}


 Identify an API that can help me classify various objects in a given image efficiently and quickly.\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 an API that allows me to classify pictures of animals with high accuracy.\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='resnext101_ibn_a', pretrained=True)", "api_arguments": [{"name": "resnext101_ibn_a", "type": "str", "description": "ResNeXt-101-IBN-a model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('XingangPan/IBN-Net', 'resnext101_ibn_a', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"resnext101_ibn_a": {"Top-1 acc": 79.12, "Top-5 acc": 94.58}}}, "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 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Deep Residual Networks", "api_name": "ResNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnet152', 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', 'resnet152', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "resnet152", "top-1_error": 21.69, "top-5_error": 5.94}}, "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."}}


 Tell me an API that can predict the breed of a dog through its image.\n
 Use this API documentation for reference:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SqueezeNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='squeezenet1_1', pretrained=True)", "api_arguments": {"version": "v0.10.0", "model": ["squeezenet1_1"], "pretrained": "True"}, "python_environment_requirements": {"torch": ">=1.9.0", "torchvision": ">=0.10.0"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'squeezenet1_1', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "probabilities = torch.nn.functional.softmax(output[0], dim=0)", "print(probabilities)"], "performance": {"dataset": "ImageNet", "accuracy": {"squeezenet1_1": {"Top-1 error": 41.81, "Top-5 error": 19.38}}}, "description": "SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy."}}


 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:  {"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."}}


 What type of model is best for recognizing multiple objects in 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."}}


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


 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:  {"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."}}


 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:  {"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."}}


 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:  {"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."}}


 Recommend me an API that can compute a depth map from a single 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_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."}}


 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:  {"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."}}


 Suggest an object detection API that is suitable for implementing real-time applications like a security camera.\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."}}


 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:  {"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."}}


 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:  {"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."}}


 Retrieve an API capable of re-identifying vehicles across different cameras by using appearance invariance.\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='resnext101_ibn_a', pretrained=True)", "api_arguments": [{"name": "resnext101_ibn_a", "type": "str", "description": "ResNeXt-101-IBN-a model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('XingangPan/IBN-Net', 'resnext101_ibn_a', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"resnext101_ibn_a": {"Top-1 acc": 79.12, "Top-5 acc": 94.58}}}, "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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "ResNeSt", "api_call": "torch.hub.load(repo_or_dir='zhanghang1989/ResNeSt', model='resnest50', pretrained=True)", "api_arguments": "resnest50", "python_environment_requirements": {"torch": "1.0.0", "torchvision": "0.2.2"}, "example_code": ["import torch", "model = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"ResNeSt-50": 81.03}}, "description": "ResNeSt models are from the ResNeSt: Split-Attention Networks paper. They are a new ResNet variant that enables attention across feature-map groups. By stacking Split-Attention blocks ResNet-style, ResNeSt models outperform other networks with similar model complexities, and also help downstream tasks including object detection, instance segmentation, and semantic segmentation."}}


 Show me an API that can efficiently classify images 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."}}


 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:  {"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."}}


 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:  {"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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Deep Residual Networks", "api_name": "ResNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnet50', 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', 'resnet50', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "resnet50", "top-1_error": 23.85, "top-5_error": 7.13}}, "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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SNNMLP", "api_call": "torch.hub.load(repo_or_dir='huawei-noah/Efficient-AI-Backbones', model='snnmlp_b', pretrained=True)", "api_arguments": [{"name": "snnmlp_b", "type": "str", "description": "SNNMLP Base model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('huawei-noah/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "SNNMLP Base", "top-1": 85.59}}, "description": "SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively."}}


 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:  {"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."}}


 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:  {"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."}}


 Is there any API that can identify plants from an image I provide?\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='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."}}


 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:  {"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'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:  {"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."}}


 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:  {"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."}}


 Give me an API that can predict the category of an object given its image.\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."}}


 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:  {"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 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:  {"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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "ResNeSt", "api_call": "torch.hub.load(repo_or_dir='zhanghang1989/ResNeSt', model='resnest101', pretrained=True)", "api_arguments": "resnest101", "python_environment_requirements": {"torch": "1.0.0", "torchvision": "0.2.2"}, "example_code": ["import torch", "model = torch.hub.load('zhanghang1989/ResNeSt', 'resnest101', pretrained=True)", "model.eval()"], "performance": {"dataset": "ImageNet", "accuracy": {"ResNeSt-101": 82.83}}, "description": "ResNeSt models are from the ResNeSt: Split-Attention Networks paper. They are a new ResNet variant that enables attention across feature-map groups. By stacking Split-Attention blocks ResNet-style, ResNeSt models outperform other networks with similar model complexities, and also help downstream tasks including object detection, instance segmentation, and semantic segmentation."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "ResNext", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='resnext101_32x4d', pretrained=True)", "api_arguments": {"pretrained": "True"}, "python_environment_requirements": ["torch", "torchvision", "PIL"], "example_code": ["import torch", "from PIL import Image", "from torchvision import transforms", "model = torch.hub.load('pytorch/vision:v0.10.0', 'resnext101_32x4d', pretrained=True)", "model.eval()", "input_image = Image.open('dog.jpg')", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "probabilities = torch.nn.functional.softmax(output[0], dim=0)"], "performance": {"dataset": "ImageNet", "resnext101_32x8d": {"top-1": 20.69, "top-5": 5.47}}, "description": "ResNext is a next-generation ResNet architecture for image classification. It is more efficient and accurate than the original ResNet. This implementation includes two versions of the model, resnext50_32x4d and resnext101_32x8d, with 50 and 101 layers respectively."}}


 Generate an API that performs image classification using a small model with low computational requirements.\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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SNNMLP", "api_call": "torch.hub.load(repo_or_dir='huawei-noah/Efficient-AI-Backbones', model='snnmlp_b', pretrained=True)", "api_arguments": [{"name": "snnmlp_b", "type": "str", "description": "SNNMLP Base model"}], "python_environment_requirements": ["torch", "torchvision", "PIL", "urllib"], "example_code": ["import torch", "model = torch.hub.load('huawei-noah/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "print(torch.nn.functional.softmax(output[0], dim=0))"], "performance": {"dataset": "ImageNet", "accuracy": {"model": "SNNMLP Base", "top-1": 85.59}}, "description": "SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "SqueezeNet", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='squeezenet1_1', pretrained=True)", "api_arguments": {"version": "v0.10.0", "model": ["squeezenet1_1"], "pretrained": "True"}, "python_environment_requirements": {"torch": ">=1.9.0", "torchvision": ">=0.10.0"}, "example_code": ["import torch", "model = torch.hub.load('pytorch/vision:v0.10.0', 'squeezenet1_1', pretrained=True)", "model.eval()", "from PIL import Image", "from torchvision import transforms", "input_image = Image.open(filename)", "preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)", "if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)", "probabilities = torch.nn.functional.softmax(output[0], dim=0)", "print(probabilities)"], "performance": {"dataset": "ImageNet", "accuracy": {"squeezenet1_1": {"Top-1 error": 41.81, "Top-5 error": 19.38}}}, "description": "SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Image Classification", "api_name": "ShuffleNet v2", "api_call": "torch.hub.load(repo_or_dir='pytorch/vision', model='shufflenet_v2_x1_0', pretrained=True)", "api_arguments": {"pretrained": "True"}, "python_environment_requirements": {"torch": "torch", "torchvision": "torchvision", "PIL": "Image", "urllib": "urllib"}, "example_code": {"import_libraries": ["import torch", "from PIL import Image", "from torchvision import transforms", "import urllib"], "load_model": ["model = torch.hub.load('pytorch/vision:v0.10.0', 'shufflenet_v2_x1_0', pretrained=True)", "model.eval()"], "load_image": ["url, filename = ('https://github.com/pytorch/hub/raw/master/images/dog.jpg', 'dog.jpg')", "try: urllib.URLopener().retrieve(url, filename)", "except: urllib.request.urlretrieve(url, filename)", "input_image = Image.open(filename)"], "preprocess_image": ["preprocess = transforms.Compose([", " transforms.Resize(256),", " transforms.CenterCrop(224),", " transforms.ToTensor(),", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),", "])", "input_tensor = preprocess(input_image)", "input_batch = input_tensor.unsqueeze(0)"], "run_inference": ["if torch.cuda.is_available():", " input_batch = input_batch.to('cuda')", " model.to('cuda')", "with torch.no_grad():", " output = model(input_batch)"], "get_probabilities": ["probabilities = torch.nn.functional.softmax(output[0], dim=0)"], "top_categories": ["top5_prob, top5_catid = torch.topk(probabilities, 5)", "for i in range(top5_prob.size(0)):", " print(categories[top5_catid[i]], top5_prob[i].item())"]}, "performance": {"dataset": "Imagenet", "accuracy": {"top-1_error": 30.64, "top-5_error": 11.68}}, "description": "ShuffleNet V2 is an efficient ConvNet optimized for speed and memory, pre-trained on Imagenet. It is designed based on practical guidelines for efficient network design, including speed and accuracy tradeoff."}}


 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:  {"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."}}


 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:  {"domain": "Classification", "framework": "PyTorch", "functionality": "Semi-supervised and semi-weakly supervised ImageNet Models", "api_name": "torch.hub.load", "api_call": "torch.hub.load(repo_or_dir='facebookresearch/semi-supervised-ImageNet1K-models', model='resnet18_swsl', pretrained=True)", "api_arguments": {"repository": "facebookresearch/semi-supervised-ImageNet1K-models", "model": "resnet18_swsl", "pretrained": "True"}, "python_environment_requirements": ["torch", "torchvision"], "example_code": ["import torch", "model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)", "model.eval()"], "performance": {"description": "Semi-supervised and semi-weakly supervised ImageNet models achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture."}, "description": "Semi-supervised and semi-weakly supervised ImageNet Models are introduced in the 'Billion scale semi-supervised learning for image classification' paper. These models are pretrained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset. They are capable of classifying images into different categories and are provided by the Facebook Research library."}}


 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:  {"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."}}


 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:  {"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."}}


 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:  {"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 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:  {"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."}}


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


 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:  {"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."}}


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


 Help me identify various objects in an image. Suggest an API for performing image classification.\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."}}


 A marketing company needs an API to classify images into animals and assign them different categories. Which API would you recommend them?\n
 Use this API documentation for reference:  {"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."}}


 Recommend an API for a mobile app that can identify fruits from images taken by the users.\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."}}