{"question_id": 1, "text": " What is an API that can be used to classify sports activities in videos?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'3D ResNet\\', \\'api_name\\': \\'slow_r50\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/pytorchvideo\\', model=\\'slow_r50\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'json\\', \\'urllib\\', \\'pytorchvideo\\', \\'torchvision\\', \\'torchaudio\\', \\'torchtext\\', \\'torcharrow\\', \\'TorchData\\', \\'TorchRec\\', \\'TorchServe\\', \\'PyTorch on XLA Devices\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'facebookresearch/pytorchvideo\\', \\'slow_r50\\', pretrained=True)\", \"device = \\'cpu\\'\", \\'model = model.eval()\\', \\'model = model.to(device)\\'], \\'performance\\': {\\'dataset\\': \\'Kinetics 400\\', \\'accuracy\\': {\\'top_1\\': 74.58, \\'top_5\\': 91.63}, \\'Flops (G)\\': 54.52, \\'Params (M)\\': 32.45}, \\'description\\': \"The 3D ResNet model is a Resnet-style video classification network pretrained on the Kinetics 400 dataset. It is based on the architecture from the paper \\'SlowFast Networks for Video Recognition\\' by Christoph Feichtenhofer et al.\"}', metadata={})]", "category": "generic"}
{"question_id": 2, "text": " Identify an API capable of converting spoken language in a recording to text.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 3, "text": " To analyze street photos, I need to segment different objects like pedestrians, vehicles, and buildings from a given image. Provide an API able to perform semantic segmentation on images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 4, "text": " To implement a lightweight object detection, I'm looking for a pre-trained model API that can detect and classify objects within an image in real-time.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 5, "text": " I need an image classification API that can handle millions of public images with thousands of hashtags. Please recommend one.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 6, "text": " Developers of a Virtual Reality event want to create a realistic digital crowd. Can you suggest a pretrained model to generate faces of celebrities?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Generative Adversarial Networks (GANs)\\', \\'api_name\\': \\'PGAN\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/pytorch_GAN_zoo:hub\\', model=\\'PGAN\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'facebookresearch/pytorch_GAN_zoo:hub\\', \\'model\\': \\'PGAN\\', \\'model_name\\': \\'celebAHQ-512\\', \\'pretrained\\': \\'True\\', \\'useGPU\\': \\'use_gpu\\'}, \\'python_environment_requirements\\': \\'Python 3\\', \\'example_code\\': {\\'import\\': \\'import torch\\', \\'use_gpu\\': \\'use_gpu = True if torch.cuda.is_available() else False\\', \\'load_model\\': \"model = torch.hub.load(\\'facebookresearch/pytorch_GAN_zoo:hub\\', \\'PGAN\\', model_name=\\'celebAHQ-512\\', pretrained=True, useGPU=use_gpu)\", \\'build_noise_data\\': \\'noise, _ = model.buildNoiseData(num_images)\\', \\'test\\': \\'generated_images = model.test(noise)\\', \\'plot_images\\': {\\'import_matplotlib\\': \\'import matplotlib.pyplot as plt\\', \\'import_torchvision\\': \\'import torchvision\\', \\'make_grid\\': \\'grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)\\', \\'imshow\\': \\'plt.imshow(grid.permute(1, 2, 0).cpu().numpy())\\', \\'show\\': \\'plt.show()\\'}}, \\'performance\\': {\\'dataset\\': \\'celebA\\', \\'accuracy\\': \\'High-quality celebrity faces\\'}, \\'description\\': \"Progressive Growing of GANs (PGAN) is a method for generating high-resolution images using generative adversarial networks. The model is trained progressively, starting with low-resolution images and gradually increasing the resolution until the desired output is achieved. This implementation is based on the paper by Tero Karras et al., \\'Progressive Growing of GANs for Improved Quality, Stability, and Variation\\'.\"}', metadata={})]", "category": "generic"}
{"question_id": 7, "text": " I need an API to classify images from a dataset with a high accuracy rate. Provide an appropriate API and the performance on the ImageNet dataset.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 8, "text": " A tourism website is building a feature to categorize photos into classes of landmarks. Recommend a machine learning API that will take an image and output which class the image falls into.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 9, "text": " A photographer at National Geographic is finding photos for the monthly magazine cover. They need a model to classify a picture of a cheetah running in the wild from other images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'GoogLeNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'googlenet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.0.0\\', \\'torchvision\\': \\'>=0.2.2\\'}, \\'example_code\\': {\\'import\\': [\\'import torch\\', \\'import urllib\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\'], \\'load_model\\': \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'googlenet\\', pretrained=True)\", \\'model_eval\\': \\'model.eval()\\', \\'image_preprocessing\\': [\\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\'], \\'model_execution\\': [\\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\'], \\'output_processing\\': [\\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\', \\'top5_prob, top5_catid = torch.topk(probabilities, 5)\\']}, \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'Top-1 error\\': \\'30.22\\', \\'Top-5 error\\': \\'10.47\\'}}, \\'description\\': \"GoogLeNet is based on a deep convolutional neural network architecture codenamed \\'Inception\\', which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).\"}', metadata={})]", "category": "generic"}
{"question_id": 10, "text": " DXmart needs to build a product image classification system for their e-commerce site. Provide an API that can classify product images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Dense Convolutional Network\\', \\'api_name\\': \\'Densenet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'densenet161\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'densenet161\\', \\'type\\': \\'str\\', \\'description\\': \\'Densenet-161 model\\'}], \\'python_environment_requirements\\': {\\'torch\\': \\'latest\\', \\'torchvision\\': \\'latest\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'densenet161\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'densenet161\\': {\\'Top-1 error\\': 22.35, \\'Top-5 error\\': 6.2}}}, \\'description\\': \\'Dense Convolutional Network (DenseNet) connects each layer to every other layer in a feed-forward fashion. It alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and substantially reduces the number of parameters.\\'}', metadata={})]", "category": "generic"}
{"question_id": 11, "text": " Identify an API to perform efficient animal classification from user provided images without sacrificing model accuracy for a biodiversity conservation project.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_efficientnet_b0\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ EfficientNet-B0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'5.29M\\', \\'top1\\': \\'78.29\\', \\'top5\\': \\'93.95\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 12, "text": " Recommend an API to build an Image Classifier that would better classify images with minimal computational resources.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'ResNext\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'resnext50_32x4d\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\'], \\'example_code\\': [\\'import torch\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'resnext50_32x4d\\', pretrained=True)\", \\'model.eval()\\', \"input_image = Image.open(\\'dog.jpg\\')\", \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'resnext50_32x4d\\': {\\'top-1\\': 22.38, \\'top-5\\': 6.3}}}, \\'description\\': \\'ResNext is a next-generation ResNet architecture for image classification. It is more efficient and accurate than the original ResNet. This implementation includes two versions of the model, resnext50_32x4d and resnext101_32x8d, with 50 and 101 layers respectively.\\'}', metadata={})]", "category": "generic"}
{"question_id": 13, "text": " I need to recognize dogs and cats from images. What API should I use to perform this task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16\\': {\\'Top-1 error\\': 28.41, \\'Top-5 error\\': 9.62}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 14, "text": " I need a suitable PyTorch API that can classify a wide range of images. Please provide me with instructions on how to load the pretrained model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 15, "text": " I need to build an image classifier to identify objects in a photo. Suggest a suitable model that I can use for this purpose.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'SqueezeNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'squeezenet1_1\\', pretrained=True)\", \\'api_arguments\\': {\\'version\\': \\'v0.10.0\\', \\'model\\': [\\'squeezenet1_1\\'], \\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'squeezenet1_1\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\', \\'print(probabilities)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'squeezenet1_1\\': {\\'Top-1 error\\': 41.81, \\'Top-5 error\\': 19.38}}}, \\'description\\': \\'SqueezeNet is an image classification model that achieves AlexNet-level accuracy with 50x fewer parameters. It has two versions: squeezenet1_0 and squeezenet1_1, with squeezenet1_1 having 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 16, "text": " A developer is building a mobile app to identify objects using the mobile camera. Suggest an API to classify object types given an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MobileNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'mobilenet_v2\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision\\', \\'mobilenet_v2\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 28.12, \\'top-5_error\\': 9.71}}, \\'description\\': \\'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\\'}', metadata={})]", "category": "generic"}
{"question_id": 17, "text": " A wildlife organization is looking to classify photos taken on their CCTV cameras into 100 different animal species. Suggest an API to achieve this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 18, "text": " A self-driving car company is developing an autonomous vehicle that requires detecting objects, drivable area segmentation, and lane detection in real-time. Suggest an appropriate API for this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 19, "text": " I want an ML library that can determine the object distances in a photo without inputting more than one photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Hybrid\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Hybrid\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 20, "text": " I would like a simple method to turn spoken user commands into text, which AI API would you recommend?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 21, "text": " Write me an API to use as a pretrained model for classifying images into categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 22, "text": " A company wants to segment objects in the images for its e-commerce website. Give an API that can segment objects in images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 23, "text": " I'm working on a medical app and I want to classify images of skin lesions. Show me an API that can classify images with high efficiency and accuracy.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_efficientnet_b0\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ EfficientNet-B0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'5.29M\\', \\'top1\\': \\'78.29\\', \\'top5\\': \\'93.95\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 24, "text": " What is an API that can classify an image of a dog into its specific breed from a list of 120 unique breeds?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 25, "text": " Can you give me an API that can classify food dishes in restaurant menus using image classification?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50_cutmix\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50_cutmix\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 + CutMix w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.98\\', \\'top5\\': \\'95.35\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 26, "text": " For my mobile app, I need an efficient and light-weight model that can classify animals, plants, landmarks, etc. in an image fed via the device's camera. Suggest an API.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_efficientnet_b0\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ EfficientNet-B0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'5.29M\\', \\'top1\\': \\'78.29\\', \\'top5\\': \\'93.95\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 27, "text": " For a wildlife photography website, suggest an API that can classify the animal species in a given photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 28, "text": " Please suggest an API that can detect and count the number of birds in an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 29, "text": " Identify an API that can classify images and works with spiking neural networks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 30, "text": " What is an efficient API that can be used to categorize images and has a much lighter model with fewer parameters than AlexNet?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'EfficientNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\', \\'pretrained\\'], \\'python_environment_requirements\\': [\\'validators\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nefficientnet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_efficientnet_b0\\', pretrained=True)\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\n\\\\nefficientnet.eval().to(device)\\\\n\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\n\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\\\n \\\\nresults = utils.pick_n_best(predictions=output, n=5)\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'IMAGENET\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \\'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 31, "text": " Find me an API which will help identifying animals in a given image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 32, "text": " My company is building a chatbot for a car dealership and we need a machine learning model that can classify cars from images. Can you suggest one?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 33, "text": " A wildlife conservationist wants to classify animals in their natural habitat with a high accuracy. Recommend an API that can assist in this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 34, "text": " A software engineer working at a computer vision company is looking for a model that can classify images efficiently on NVIDIA GPUs. Provide an API recommendation.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'EfficientNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\', \\'pretrained\\'], \\'python_environment_requirements\\': [\\'validators\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nefficientnet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_efficientnet_b0\\', pretrained=True)\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\n\\\\nefficientnet.eval().to(device)\\\\n\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\n\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\\\n \\\\nresults = utils.pick_n_best(predictions=output, n=5)\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'IMAGENET\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \\'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 35, "text": " Recommend an API to translate an English ebook to French.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Semantic Segmentation\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Neural Machine Translation\\', \\'api_name\\': \\'Transformer (NMT)\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/fairseq\\')\", \\'api_arguments\\': [\\'model_name\\', \\'tokenizer\\', \\'bpe\\', \\'beam\\', \\'sampling\\', \\'sampling_topk\\'], \\'python_environment_requirements\\': [\\'bitarray\\', \\'fastBPE\\', \\'hydra-core\\', \\'omegaconf\\', \\'regex\\', \\'requests\\', \\'sacremoses\\', \\'subword_nmt\\'], \\'example_code\\': \"import torch\\\\n\\\\nen2fr = torch.hub.load(\\'pytorch/fairseq\\', \\'transformer.wmt14.en-fr\\', tokenizer=\\'moses\\', bpe=\\'subword_nmt\\')\\\\n\\\\nen2fr.cuda()\\\\n\\\\nfr = en2fr.translate(\\'Hello world!\\', beam=5)\\\\nassert fr == \\'Bonjour \u00e0 tous !\\'\", \\'performance\\': {\\'dataset\\': [{\\'name\\': \"WMT\\'14\", \\'accuracy\\': \\'Not provided\\'}, {\\'name\\': \"WMT\\'18\", \\'accuracy\\': \\'Not provided\\'}, {\\'name\\': \"WMT\\'19\", \\'accuracy\\': \\'Not provided\\'}]}, \\'description\\': \"Transformer (NMT) is a powerful sequence-to-sequence modeling architecture that produces state-of-the-art neural machine translation systems. It is based on the paper \\'Attention Is All You Need\\' and has been improved using techniques such as large-scale semi-supervised training, back-translation, and noisy-channel reranking. It supports English-French and English-German translation as well as round-trip translation for paraphrasing.\"}', metadata={})]", "category": "generic"}
{"question_id": 36, "text": " In an attempt to streamline content moderation, Facebook is implementing an AI-enabled tool to identify potentially inappropriate images. Suggest an API that can recognize objects within an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Semi-supervised and semi-weakly supervised ImageNet Models\\', \\'api_name\\': \\'torch.hub.load\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/semi-supervised-ImageNet1K-models\\', model=\\'resnet18_swsl\\', pretrained=True)\", \\'api_arguments\\': {\\'repository\\': \\'facebookresearch/semi-supervised-ImageNet1K-models\\', \\'model\\': \\'resnet18_swsl\\', \\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'facebookresearch/semi-supervised-ImageNet1K-models\\', \\'resnet18_swsl\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'description\\': \\'Semi-supervised and semi-weakly supervised ImageNet models achieve state-of-the-art accuracy of 81.2% on ImageNet for the widely used/adopted ResNet-50 model architecture.\\'}, \\'description\\': \"Semi-supervised and semi-weakly supervised ImageNet Models are introduced in the \\'Billion scale semi-supervised learning for image classification\\' paper. These models are pretrained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset. They are capable of classifying images into different categories and are provided by the Facebook Research library.\"}', metadata={})]", "category": "generic"}
{"question_id": 37, "text": " The weatherman needs an AI which could read out the daily weather information. Tell me an API that generates spoken weather information from a written weather forecast.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 38, "text": " A developer needs to classify images using a model that does not require additional tricks for high accuracy. Recommend an API with a high top-1 accuracy without using any tricks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 39, "text": " I need an API that can help me identify the type of a cucumber. It should be able to tell me whether it's pickling, slicing, or burpless cucumber.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50_cutmix\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50_cutmix\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 + CutMix w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.98\\', \\'top5\\': \\'95.35\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 40, "text": " I need to develop a self-driving car which can simultaneously recognize objects, drivable areas, and lanes. Recommend me an API to handle these tasks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 41, "text": " I'd like to detect voice activity in an audio file. What API can help me perform this task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Voice Activity Detection\\', \\'api_name\\': \\'Silero Voice Activity Detector\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-vad\\', model=\\'silero_vad\\', force_reload=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-vad\\', \\'model\\': \\'silero_vad\\', \\'force_reload\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torchaudio\\': \\'pip install -q torchaudio\\'}, \\'example_code\\': {\\'import\\': [\\'import torch\\', \\'torch.set_num_threads(1)\\', \\'from IPython.display import Audio\\', \\'from pprint import pprint\\'], \\'download_example\\': \"torch.hub.download_url_to_file(\\'https://models.silero.ai/vad_models/en.wav\\', \\'en_example.wav\\')\", \\'load_model\\': \"model, utils = torch.hub.load(repo_or_dir=\\'snakers4/silero-vad\\', model=\\'silero_vad\\', force_reload=True)\", \\'load_utils\\': \\'(get_speech_timestamps, _, read_audio, _) = utils\\', \\'set_sampling_rate\\': \\'sampling_rate = 16000\\', \\'read_audio\\': \"wav = read_audio(\\'en_example.wav\\', sampling_rate=sampling_rate)\", \\'get_speech_timestamps\\': \\'speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=sampling_rate)\\', \\'print_speech_timestamps\\': \\'pprint(speech_timestamps)\\'}, \\'performance\\': {\\'dataset\\': \\'\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'Silero VAD is a pre-trained enterprise-grade Voice Activity Detector (VAD) that aims to provide a high-quality and modern alternative to the WebRTC Voice Activity Detector. The model is optimized for performance on 1 CPU thread and is quantized.\\'}', metadata={})]", "category": "generic"}
{"question_id": 42, "text": " We wish to create an app to make coloring books from images. Recommend an API to extract the regions that should be colored.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 43, "text": " Imagine you were given a set of images and you need to tell what objects are on the pictures. Indicate an API that can classify the objects in the images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 44, "text": " My friend recommended the Densenet-201 model to classify images. Find an API that I can use for this model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Dense Convolutional Network\\', \\'api_name\\': \\'Densenet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'densenet201\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'densenet201\\', \\'type\\': \\'str\\', \\'description\\': \\'Densenet-201 model\\'}], \\'python_environment_requirements\\': {\\'torch\\': \\'latest\\', \\'torchvision\\': \\'latest\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'densenet201\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'densenet201\\': {\\'Top-1 error\\': 22.8, \\'Top-5 error\\': 6.43}}}, \\'description\\': \\'Dense Convolutional Network (DenseNet) connects each layer to every other layer in a feed-forward fashion. It alleviates the vanishing-gradient problem, strengthens feature propagation, encourages feature reuse, and substantially reduces the number of parameters.\\'}', metadata={})]", "category": "generic"}
{"question_id": 45, "text": " Provide me with an API that can segment objects within an image into separate categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 46, "text": " Looking for a fast and efficient image classification API to suit my low-end device. What would you recommend?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'EfficientNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\', \\'pretrained\\'], \\'python_environment_requirements\\': [\\'validators\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nefficientnet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_efficientnet_b0\\', pretrained=True)\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\n\\\\nefficientnet.eval().to(device)\\\\n\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\n\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\\\n \\\\nresults = utils.pick_n_best(predictions=output, n=5)\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'IMAGENET\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \\'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 47, "text": " I need a model that can help identify which domain an image belongs to, such as artistic style or natural scenery. Recommend me an API that can do this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 48, "text": " I want to know which dog breed a given image belongs to. Tell me an API that is capable of identifying dog breeds.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 49, "text": " I need to classify images into various categories based on their content. Can you suggest an API that can do this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 50, "text": " Recommend an API to automatically fine-tune a neural network's architecture for optimal performance on a specific graphics processing unit (GPU) platform.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'GPUNet Networks\\', \\'api_name\\': \\'torch.hub.load\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_gpunet\\', pretrained=True, model_type=model_type, model_math=precision)\", \\'api_arguments\\': {\\'repository\\': \\'NVIDIA/DeepLearningExamples:torchhub\\', \\'model\\': \\'nvidia_gpunet\\', \\'pretrained\\': \\'True\\', \\'model_type\\': \\'GPUNet-0\\', \\'model_math\\': \\'fp32\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'validators\\', \\'matplotlib\\', \\'timm==0.5.4\\'], \\'example_code\\': [\\'import torch\\', \"model_type = \\'GPUNet-0\\'\", \"precision = \\'fp32\\'\", \"gpunet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_gpunet\\', pretrained=True, model_type=model_type, model_math=precision)\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'gpunet.to(device)\\', \\'gpunet.eval()\\'], \\'performance\\': {\\'dataset\\': \\'IMAGENET\\', \\'description\\': \\'GPUNet demonstrates state-of-the-art inference performance up to 2x faster than EfficientNet-X and FBNet-V3.\\'}, \\'description\\': \\'GPUNet is a family of Convolutional Neural Networks designed by NVIDIA using novel Neural Architecture Search (NAS) methods. They are optimized for NVIDIA GPU and TensorRT performance. GPUNet models are pretrained on the IMAGENET dataset and are capable of classifying images into different categories. The models are provided by the NVIDIA Deep Learning Examples library.\\'}', metadata={})]", "category": "generic"}
{"question_id": 51, "text": " A software engineer is trying to determine if an image contains a dog, cat or a horse. Identify an API that could be fine-tuned to achieve the objective.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 52, "text": " Can you suggest me an AI model that can classify images with 50x fewer parameters than AlexNet and better performance on a robotics project I'm working on?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 53, "text": " Recommend a way to recognize decorative and architectural elements in architectural design images using a pre-trained network.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 54, "text": " Can you suggest an API that can automatically classify images for me?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 55, "text": " Suggest an API for classifying dog breeds given an image of a dog.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 56, "text": " Suggest an API designed for NVIDIA GPU and TensorRT performance optimization to classify images into different categories.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'SE-ResNeXt101\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_se_resnext101_32x4d\\', pretrained=True)\", \\'api_arguments\\': \\'N/A\\', \\'python_environment_requirements\\': \\'validators, matplotlib\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nimport torchvision.transforms as transforms\\\\nimport numpy as np\\\\nimport json\\\\nimport requests\\\\nimport matplotlib.pyplot as plt\\\\nimport warnings\\\\nwarnings.filterwarnings(\\'ignore\\')\\\\n%matplotlib inline\\\\ndevice = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\\\\nprint(f\\'Using {device} for inference\\')\\\\nresneXt = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_se_resnext101_32x4d\\')\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\nresneXt.eval().to(device)\\\\nuris = [\\'http://images.cocodataset.org/test-stuff2017/000000024309.jpg\\',\\'http://images.cocodataset.org/test-stuff2017/000000028117.jpg\\',\\'http://images.cocodataset.org/test-stuff2017/000000006149.jpg\\',\\'http://images.cocodataset.org/test-stuff2017/000000004954.jpg\\']\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(resneXt(batch), dim=1)\\\\nresults = utils.pick_n_best(predictions=output, n=5)\\\\nfor uri, result in zip(uris, results):\\\\n img = Image.open(requests.get(uri, stream=True).raw)\\\\n img.thumbnail((256,256), Image.ANTIALIAS)\\\\n plt.imshow(img)\\\\n plt.show()\\\\n print(result)\", \\'performance\\': {\\'dataset\\': \\'IMAGENET\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'The SE-ResNeXt101-32x4d is a ResNeXt101-32x4d model with added Squeeze-and-Excitation module. This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures, which allows researchers to get results 3x faster than training without Tensor Cores while experiencing the benefits of mixed precision training.\\'}', metadata={})]", "category": "generic"}
{"question_id": 57, "text": " Translate the given English text to French using machine learning API.\\n###Input: {\\\"text\\\": \\\"I like playing basketball.\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Semantic Segmentation\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Neural Machine Translation\\', \\'api_name\\': \\'Transformer (NMT)\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/fairseq\\')\", \\'api_arguments\\': [\\'model_name\\', \\'tokenizer\\', \\'bpe\\', \\'beam\\', \\'sampling\\', \\'sampling_topk\\'], \\'python_environment_requirements\\': [\\'bitarray\\', \\'fastBPE\\', \\'hydra-core\\', \\'omegaconf\\', \\'regex\\', \\'requests\\', \\'sacremoses\\', \\'subword_nmt\\'], \\'example_code\\': \"import torch\\\\n\\\\nen2fr = torch.hub.load(\\'pytorch/fairseq\\', \\'transformer.wmt14.en-fr\\', tokenizer=\\'moses\\', bpe=\\'subword_nmt\\')\\\\n\\\\nen2fr.cuda()\\\\n\\\\nfr = en2fr.translate(\\'Hello world!\\', beam=5)\\\\nassert fr == \\'Bonjour \u00e0 tous !\\'\", \\'performance\\': {\\'dataset\\': [{\\'name\\': \"WMT\\'14\", \\'accuracy\\': \\'Not provided\\'}, {\\'name\\': \"WMT\\'18\", \\'accuracy\\': \\'Not provided\\'}, {\\'name\\': \"WMT\\'19\", \\'accuracy\\': \\'Not provided\\'}]}, \\'description\\': \"Transformer (NMT) is a powerful sequence-to-sequence modeling architecture that produces state-of-the-art neural machine translation systems. It is based on the paper \\'Attention Is All You Need\\' and has been improved using techniques such as large-scale semi-supervised training, back-translation, and noisy-channel reranking. It supports English-French and English-German translation as well as round-trip translation for paraphrasing.\"}', metadata={})]", "category": "generic"}
{"question_id": 58, "text": " Recommend an API to identify the breed of a dog from a picture input.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13_bn\\': {\\'Top-1 error\\': 28.45, \\'Top-5 error\\': 9.63}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 59, "text": " I want to build an image classifier to boost the accuracy of the Vanilla Resnet-50 model on ImageNet data without using any data augmentation tricks. What API should I use?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 60, "text": " Create a 3D reconstruction of a scene from only one image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'MiDaS_small\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'MiDaS_small\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 61, "text": " A video editor is developing a software that will allow users to mute specific instruments in a song. Provide an API that can separate audio into multiple tracks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Audio Separation\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Music Source Separation\\', \\'api_name\\': \\'Open-Unmix\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'sigsep/open-unmix-pytorch\\', model=\\'umxhq\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\'], \\'python_environment_requirements\\': [\\'PyTorch >=1.6.0\\', \\'torchaudio\\'], \\'example_code\\': [\\'import torch\\', \"separator = torch.hub.load(\\'sigsep/open-unmix-pytorch\\', \\'umxhq\\')\", \\'audio = torch.rand((1, 2, 100000))\\', \\'original_sample_rate = separator.sample_rate\\', \\'estimates = separator(audio)\\'], \\'performance\\': {\\'dataset\\': \\'MUSDB18\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'Open-Unmix provides ready-to-use models that allow users to separate pop music into four stems: vocals, drums, bass and the remaining other instruments. The models were pre-trained on the freely available MUSDB18 dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 62, "text": " I am working on a project where I need to convert a text document into an audio file. Can you suggest an API for text-to-speech conversion?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 63, "text": " Suggest an API for identifying objects in a picture taken at a city park.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 64, "text": " I have an image and I need to detect the different objects in that image. Give me an API that can do this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 65, "text": " I want to create a new collection of clothing designs. Recommend an API that can generate unique images of clothing items.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 66, "text": " I'm working on an image classification project where I need to identify the contents of an image. Can you suggest an API for that?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16\\': {\\'Top-1 error\\': 28.41, \\'Top-5 error\\': 9.62}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 67, "text": " List an API that will allow me to input text that will be transformed into an audio file.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 68, "text": " Find a model that is optimal for the task of person re-identification from a set of images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Hybrid\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Hybrid\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 69, "text": " Query an API that carries out vehicle or person re-identification tasks accurately.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 70, "text": " I need an image classification model that can classify objects in images with high accuracy. Suggest me an API.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 71, "text": " Help me find a way to classify different species of birds given an image from the Internet.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'GoogLeNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'googlenet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.0.0\\', \\'torchvision\\': \\'>=0.2.2\\'}, \\'example_code\\': {\\'import\\': [\\'import torch\\', \\'import urllib\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\'], \\'load_model\\': \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'googlenet\\', pretrained=True)\", \\'model_eval\\': \\'model.eval()\\', \\'image_preprocessing\\': [\\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\'], \\'model_execution\\': [\\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\'], \\'output_processing\\': [\\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\', \\'top5_prob, top5_catid = torch.topk(probabilities, 5)\\']}, \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'Top-1 error\\': \\'30.22\\', \\'Top-5 error\\': \\'10.47\\'}}, \\'description\\': \"GoogLeNet is based on a deep convolutional neural network architecture codenamed \\'Inception\\', which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).\"}', metadata={})]", "category": "generic"}
{"question_id": 72, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50_cutmix\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50_cutmix\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 + CutMix w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.98\\', \\'top5\\': \\'95.35\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 73, "text": " I want to recognize objects in an image. Can you find me an API that can do this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 74, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 75, "text": " I want to classify images accurately without latency. Help me find an API to do that.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 76, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 77, "text": " A retailer would like to better categorize images of products on their website. Provide a model API that can perform image classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 78, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 79, "text": " I need a Python library for calculating relative depth from a single image. What do you suggest?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Hybrid\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Hybrid\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 80, "text": " Tell me an API that I can use to classify images into different categories using a pre-trained ResNet50 model.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'ResNet50\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_resnet50\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'pip install validators matplotlib\\'], \\'example_code\\': [\\'import torch\\', \\'from PIL import Image\\', \\'import torchvision.transforms as transforms\\', \\'import numpy as np\\', \\'import json\\', \\'import requests\\', \\'import matplotlib.pyplot as plt\\', \\'import warnings\\', \"warnings.filterwarnings(\\'ignore\\')\", \\'%matplotlib inline\\', \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \"print(f\\'Using {device} for inference\\')\", \"resnet50 = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_resnet50\\', pretrained=True)\", \"utils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\", \\'resnet50.eval().to(device)\\', \\'uris = [...]\\', \\'batch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\', \\'with torch.no_grad():\\', \\' output = torch.nn.functional.softmax(resnet50(batch), dim=1)\\', \\'results = utils.pick_n_best(predictions=output, n=5)\\', \\'for uri, result in zip(uris, results):\\', \\' img = Image.open(requests.get(uri, stream=True).raw)\\', \\' img.thumbnail((256,256), Image.ANTIALIAS)\\', \\' plt.imshow(img)\\', \\' plt.show()\\', \\' print(result)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': \\'~0.5% top1 improvement over ResNet50 v1\\'}, \\'description\\': \\'The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model. The difference between v1 and v1.5 is that, in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec). The model is initialized as described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures.\\'}', metadata={})]", "category": "generic"}
{"question_id": 81, "text": " I am developing an app for bird species classification. Suggest me an API that can identify bird species in images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.67\\', \\'top5\\': \\'95.09\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 82, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50_cutmix\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50_cutmix\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 + CutMix w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.98\\', \\'top5\\': \\'95.35\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 83, "text": " Identify an API that can help me classify various objects in a given image efficiently and quickly.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 84, "text": " Find an API that allows me to classify pictures of animals with high accuracy.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 85, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 86, "text": " Tell me an API that can predict the breed of a dog through its image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50_cutmix\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50_cutmix\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 + CutMix w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.98\\', \\'top5\\': \\'95.35\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 87, "text": " A wildlife researcher wants to identify different bird species from a picture. Suggest a deep learning model that can help them achieve this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 88, "text": " What type of model is best for recognizing multiple objects in images? \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Single Shot MultiBox Detector\\', \\'api_name\\': \\'SSD\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_ssd\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\'], \\'python_environment_requirements\\': [\\'numpy\\', \\'scipy\\', \\'scikit-image\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nssd_model = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_ssd\\')\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_ssd_processing_utils\\')\\\\n\\\\nssd_model.to(\\'cuda\\')\\\\nssd_model.eval()\\\\n\\\\ninputs = [utils.prepare_input(uri) for uri in uris]\\\\ntensor = utils.prepare_tensor(inputs)\\\\n\\\\nwith torch.no_grad():\\\\n detections_batch = ssd_model(tensor)\\\\n\\\\nresults_per_input = utils.decode_results(detections_batch)\\\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'COCO\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \"The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \\'SSD: Single Shot MultiBox Detector\\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency.\"}', metadata={})]", "category": "generic"}
{"question_id": 89, "text": " Find the species of an animal in a given photo using an API.\\n###Input: \\\"zebra.jpg\\\"\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 90, "text": " I need to classify images on different edge devices with various resource constraints. Suggest an API suitable for this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'SE-ResNeXt101\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_se_resnext101_32x4d\\', pretrained=True)\", \\'api_arguments\\': \\'N/A\\', \\'python_environment_requirements\\': \\'validators, matplotlib\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nimport torchvision.transforms as transforms\\\\nimport numpy as np\\\\nimport json\\\\nimport requests\\\\nimport matplotlib.pyplot as plt\\\\nimport warnings\\\\nwarnings.filterwarnings(\\'ignore\\')\\\\n%matplotlib inline\\\\ndevice = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\\\\nprint(f\\'Using {device} for inference\\')\\\\nresneXt = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_se_resnext101_32x4d\\')\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\nresneXt.eval().to(device)\\\\nuris = [\\'http://images.cocodataset.org/test-stuff2017/000000024309.jpg\\',\\'http://images.cocodataset.org/test-stuff2017/000000028117.jpg\\',\\'http://images.cocodataset.org/test-stuff2017/000000006149.jpg\\',\\'http://images.cocodataset.org/test-stuff2017/000000004954.jpg\\']\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(resneXt(batch), dim=1)\\\\nresults = utils.pick_n_best(predictions=output, n=5)\\\\nfor uri, result in zip(uris, results):\\\\n img = Image.open(requests.get(uri, stream=True).raw)\\\\n img.thumbnail((256,256), Image.ANTIALIAS)\\\\n plt.imshow(img)\\\\n plt.show()\\\\n print(result)\", \\'performance\\': {\\'dataset\\': \\'IMAGENET\\', \\'accuracy\\': \\'N/A\\'}, \\'description\\': \\'The SE-ResNeXt101-32x4d is a ResNeXt101-32x4d model with added Squeeze-and-Excitation module. This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures, which allows researchers to get results 3x faster than training without Tensor Cores while experiencing the benefits of mixed precision training.\\'}', metadata={})]", "category": "generic"}
{"question_id": 91, "text": " Provide an API for converting text to speech, since the marketing team needs to generate realistic voices for a series of advertisements.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 92, "text": " I need an API that helps classify images with the highest accuracy. Tell me an API that can achieve this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 93, "text": " Pinterest wants to build a system that can categorize images uploaded by users. What API should they use for this task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13_bn\\': {\\'Top-1 error\\': 28.45, \\'Top-5 error\\': 9.63}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 94, "text": " Recommend me an API that can compute a depth map from a single input image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Hybrid\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Hybrid\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 95, "text": " I am working on a project that involves bird image identification. Can you recommend an API that can classify bird species from images?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 96, "text": " Suggest an object detection API that is suitable for implementing real-time applications like a security camera.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 97, "text": " A mobile application needs a machine learning model for object classification from various user images. Suggest an appropriate API for this task. \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MobileNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'mobilenet_v2\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision\\', \\'mobilenet_v2\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 28.12, \\'top-5_error\\': 9.71}}, \\'description\\': \\'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\\'}', metadata={})]", "category": "generic"}
{"question_id": 98, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13_bn\\': {\\'Top-1 error\\': 28.45, \\'Top-5 error\\': 9.63}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 99, "text": " Retrieve an API capable of re-identifying vehicles across different cameras by using appearance invariance.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'HybridNets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'datvuthanh/hybridnets\\', model=\\'hybridnets\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'Python>=3.7, PyTorch>=1.10\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'datvuthanh/hybridnets\\', \\'hybridnets\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,384)\\\\nfeatures, regression, classification, anchors, segmentation = model(img)\", \\'performance\\': {\\'dataset\\': [{\\'name\\': \\'BDD100K\\', \\'accuracy\\': {\\'Traffic Object Detection\\': {\\'Recall (%)\\': 92.8, \\'mAP@0.5 (%)\\': 77.3}, \\'Drivable Area Segmentation\\': {\\'Drivable mIoU (%)\\': 90.5}, \\'Lane Line Detection\\': {\\'Accuracy (%)\\': 85.4, \\'Lane Line IoU (%)\\': 31.6}}}]}, \\'description\\': \\'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 100, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 101, "text": " Show me an API that can efficiently classify images on mobile platforms.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MobileNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'mobilenet_v2\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision\\', \\'mobilenet_v2\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 28.12, \\'top-5_error\\': 9.71}}, \\'description\\': \\'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\\'}', metadata={})]", "category": "generic"}
{"question_id": 102, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 103, "text": " A company wants to develop a photo sharing app like Instagram. Recommend an API to recognize objects in the photos uploaded by users.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 104, "text": " Google Photos wants to create a way to classify images uploaded by users into different categories. Recommend an API for this purpose.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'GoogLeNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'googlenet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.0.0\\', \\'torchvision\\': \\'>=0.2.2\\'}, \\'example_code\\': {\\'import\\': [\\'import torch\\', \\'import urllib\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\'], \\'load_model\\': \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'googlenet\\', pretrained=True)\", \\'model_eval\\': \\'model.eval()\\', \\'image_preprocessing\\': [\\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\'], \\'model_execution\\': [\\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\'], \\'output_processing\\': [\\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\', \\'top5_prob, top5_catid = torch.topk(probabilities, 5)\\']}, \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'Top-1 error\\': \\'30.22\\', \\'Top-5 error\\': \\'10.47\\'}}, \\'description\\': \"GoogLeNet is based on a deep convolutional neural network architecture codenamed \\'Inception\\', which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).\"}', metadata={})]", "category": "generic"}
{"question_id": 105, "text": " Help me build a bird detection system. Recommend me an API that I can adapt for bird classification from photographs. \\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.67\\', \\'top5\\': \\'95.09\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 106, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16\\': {\\'Top-1 error\\': 28.41, \\'Top-5 error\\': 9.62}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 107, "text": " I want to create an AI tool that automates recognizing objects in an image. Recommend an API that can do this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 108, "text": " Is there any API that can identify plants from an image I provide?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 109, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MobileNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'mobilenet_v2\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision\\', \\'mobilenet_v2\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 28.12, \\'top-5_error\\': 9.71}}, \\'description\\': \\'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\\'}', metadata={})]", "category": "generic"}
{"question_id": 110, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 111, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'MiDaS_small\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'MiDaS_small\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 112, "text": " Give me an API that can predict the category of an object given its image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 113, "text": " Can you provide a GAN API that can generate high-quality 64x64 images for an apparel ecommerce company?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Generative Adversarial Networks (GANs)\\', \\'api_name\\': \\'PGAN\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/pytorch_GAN_zoo:hub\\', model=\\'PGAN\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'facebookresearch/pytorch_GAN_zoo:hub\\', \\'model\\': \\'PGAN\\', \\'model_name\\': \\'celebAHQ-512\\', \\'pretrained\\': \\'True\\', \\'useGPU\\': \\'use_gpu\\'}, \\'python_environment_requirements\\': \\'Python 3\\', \\'example_code\\': {\\'import\\': \\'import torch\\', \\'use_gpu\\': \\'use_gpu = True if torch.cuda.is_available() else False\\', \\'load_model\\': \"model = torch.hub.load(\\'facebookresearch/pytorch_GAN_zoo:hub\\', \\'PGAN\\', model_name=\\'celebAHQ-512\\', pretrained=True, useGPU=use_gpu)\", \\'build_noise_data\\': \\'noise, _ = model.buildNoiseData(num_images)\\', \\'test\\': \\'generated_images = model.test(noise)\\', \\'plot_images\\': {\\'import_matplotlib\\': \\'import matplotlib.pyplot as plt\\', \\'import_torchvision\\': \\'import torchvision\\', \\'make_grid\\': \\'grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)\\', \\'imshow\\': \\'plt.imshow(grid.permute(1, 2, 0).cpu().numpy())\\', \\'show\\': \\'plt.show()\\'}}, \\'performance\\': {\\'dataset\\': \\'celebA\\', \\'accuracy\\': \\'High-quality celebrity faces\\'}, \\'description\\': \"Progressive Growing of GANs (PGAN) is a method for generating high-resolution images using generative adversarial networks. The model is trained progressively, starting with low-resolution images and gradually increasing the resolution until the desired output is achieved. This implementation is based on the paper by Tero Karras et al., \\'Progressive Growing of GANs for Improved Quality, Stability, and Variation\\'.\"}', metadata={})]", "category": "generic"}
{"question_id": 114, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 115, "text": " Recommend an API that can be used for bird species recognition using pictures taken by a wildlife photographer.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 116, "text": " I am starting a startup that recommends clothing to users based on images of their outfits. What is a good API for this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13_bn\\': {\\'Top-1 error\\': 28.45, \\'Top-5 error\\': 9.63}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 117, "text": " Generate an API that performs image classification using a small model with low computational requirements.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_mobilenetv3_small_100\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_mobilenetv3_small_100\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ MobileNet V3-Small 1.0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'2.54M\\', \\'top1\\': \\'69.65\\', \\'top5\\': \\'88.71\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 118, "text": " I need an efficient AI-based classifier to identify products on grocery store shelves. Suggest an appropriate API to implement this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_efficientnet_b0\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ EfficientNet-B0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'5.29M\\', \\'top1\\': \\'78.29\\', \\'top5\\': \\'93.95\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 119, "text": " I want to perform image classification for optimizing the storage space of a database. Provide an API that enables this while maintaining accuracy.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16\\': {\\'Top-1 error\\': 28.41, \\'Top-5 error\\': 9.62}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 120, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.67\\', \\'top5\\': \\'95.09\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 121, "text": " A digital artist needs an API that can recognize and classify images containing multiple objects. Which API would you suggest?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 122, "text": " Suggest an API for a wildlife conservation organization that could help them identify animals from images captured by their research cameras.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 123, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 124, "text": " I am a developer at Audible and I am looking for an API that can convert text to speech, find something suitable.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 125, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.67\\', \\'top5\\': \\'95.09\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 126, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_mobilenetv3_small_100\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_mobilenetv3_small_100\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ MobileNet V3-Small 1.0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'2.54M\\', \\'top1\\': \\'69.65\\', \\'top5\\': \\'88.71\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 127, "text": " Can you provide me an API for classifying a video content based on the actions performed in it?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Video Classification\\', \\'framework\\': \\'PyTorchVideo\\', \\'functionality\\': \\'SlowFast Networks\\', \\'api_name\\': \\'torch.hub.load\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/pytorchvideo\\', model=\\'slowfast_r50\\', pretrained=True)\", \\'api_arguments\\': {\\'repository\\': \\'facebookresearch/pytorchvideo\\', \\'model\\': \\'slowfast_r50\\', \\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'pytorchvideo\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'facebookresearch/pytorchvideo\\', \\'slowfast_r50\\', pretrained=True)\", \"device = \\'cpu\\'\", \\'model = model.eval()\\', \\'model = model.to(device)\\'], \\'performance\\': {\\'dataset\\': \\'Kinetics 400\\', \\'accuracy\\': {\\'top1\\': 76.94, \\'top5\\': 92.69}, \\'flops\\': 65.71, \\'params\\': 34.57}, \\'description\\': \"Slow Fast model architectures are based on the paper \\'SlowFast Networks for Video Recognition\\' by Christoph Feichtenhofer et al. They are pretrained on the Kinetics 400 dataset using the 8x8 setting. This model is capable of classifying video clips into different action categories. It is provided by the FAIR PyTorchVideo library.\"}', metadata={})]", "category": "generic"}
{"question_id": 128, "text": " 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 \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'HybridNets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'datvuthanh/hybridnets\\', model=\\'hybridnets\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'Python>=3.7, PyTorch>=1.10\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'datvuthanh/hybridnets\\', \\'hybridnets\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,384)\\\\nfeatures, regression, classification, anchors, segmentation = model(img)\", \\'performance\\': {\\'dataset\\': [{\\'name\\': \\'BDD100K\\', \\'accuracy\\': {\\'Traffic Object Detection\\': {\\'Recall (%)\\': 92.8, \\'mAP@0.5 (%)\\': 77.3}, \\'Drivable Area Segmentation\\': {\\'Drivable mIoU (%)\\': 90.5}, \\'Lane Line Detection\\': {\\'Accuracy (%)\\': 85.4, \\'Lane Line IoU (%)\\': 31.6}}}]}, \\'description\\': \\'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 129, "text": " Identify an API which detects voice activity in an audio file and share the code to load it.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Voice Activity Detection\\', \\'api_name\\': \\'Silero Voice Activity Detector\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-vad\\', model=\\'silero_vad\\', force_reload=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-vad\\', \\'model\\': \\'silero_vad\\', \\'force_reload\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torchaudio\\': \\'pip install -q torchaudio\\'}, \\'example_code\\': {\\'import\\': [\\'import torch\\', \\'torch.set_num_threads(1)\\', \\'from IPython.display import Audio\\', \\'from pprint import pprint\\'], \\'download_example\\': \"torch.hub.download_url_to_file(\\'https://models.silero.ai/vad_models/en.wav\\', \\'en_example.wav\\')\", \\'load_model\\': \"model, utils = torch.hub.load(repo_or_dir=\\'snakers4/silero-vad\\', model=\\'silero_vad\\', force_reload=True)\", \\'load_utils\\': \\'(get_speech_timestamps, _, read_audio, _) = utils\\', \\'set_sampling_rate\\': \\'sampling_rate = 16000\\', \\'read_audio\\': \"wav = read_audio(\\'en_example.wav\\', sampling_rate=sampling_rate)\", \\'get_speech_timestamps\\': \\'speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=sampling_rate)\\', \\'print_speech_timestamps\\': \\'pprint(speech_timestamps)\\'}, \\'performance\\': {\\'dataset\\': \\'\\', \\'accuracy\\': \\'\\'}, \\'description\\': \\'Silero VAD is a pre-trained enterprise-grade Voice Activity Detector (VAD) that aims to provide a high-quality and modern alternative to the WebRTC Voice Activity Detector. The model is optimized for performance on 1 CPU thread and is quantized.\\'}', metadata={})]", "category": "generic"}
{"question_id": 130, "text": " Help me identify various objects in an image. Suggest an API for performing image classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 131, "text": " A marketing company needs an API to classify images into animals and assign them different categories. Which API would you recommend them?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 132, "text": " Recommend an API for a mobile app that can identify fruits from images taken by the users.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_mobilenetv3_small_100\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_mobilenetv3_small_100\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ MobileNet V3-Small 1.0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'2.54M\\', \\'top1\\': \\'69.65\\', \\'top5\\': \\'88.71\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 133, "text": " A city is planning to survey the land for urban development. Provide me with an API that can identify buildings and roads from an aerial photo.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'HybridNets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'datvuthanh/hybridnets\\', model=\\'hybridnets\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'Python>=3.7, PyTorch>=1.10\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'datvuthanh/hybridnets\\', \\'hybridnets\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,384)\\\\nfeatures, regression, classification, anchors, segmentation = model(img)\", \\'performance\\': {\\'dataset\\': [{\\'name\\': \\'BDD100K\\', \\'accuracy\\': {\\'Traffic Object Detection\\': {\\'Recall (%)\\': 92.8, \\'mAP@0.5 (%)\\': 77.3}, \\'Drivable Area Segmentation\\': {\\'Drivable mIoU (%)\\': 90.5}, \\'Lane Line Detection\\': {\\'Accuracy (%)\\': 85.4, \\'Lane Line IoU (%)\\': 31.6}}}]}, \\'description\\': \\'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 134, "text": " I need an efficient model for classifying animals in images taken by wildlife cameras. Suggest me an API for this purpose.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_efficientnet_b0\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ EfficientNet-B0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'5.29M\\', \\'top1\\': \\'78.29\\', \\'top5\\': \\'93.95\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 135, "text": " The company is creating a neural network model that can run efficiently on different hardware platforms. Tell me an API that specializes CNNs for different hardware.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'EfficientNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\', \\'pretrained\\'], \\'python_environment_requirements\\': [\\'validators\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nefficientnet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_efficientnet_b0\\', pretrained=True)\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\n\\\\nefficientnet.eval().to(device)\\\\n\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\n\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\\\n \\\\nresults = utils.pick_n_best(predictions=output, n=5)\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'IMAGENET\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \\'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 136, "text": " Farlando Corp has an application that runs on their customers' GPUs, and they want a neural network that is optimized on GPU performance. Recommend an API that they can use for image classification.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'HarDNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'PingoLH/Pytorch-HarDNet\\', model=\\'hardnet85\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'hardnet85\\', \\'type\\': \\'str\\', \\'description\\': \\'HarDNet-85 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'PingoLH/Pytorch-HarDNet\\', \\'hardnet85\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'hardnet85\\': {\\'Top-1 error\\': 21.96, \\'Top-5 error\\': 6.11}}}, \\'description\\': \\'Harmonic DenseNet (HarDNet) is a low memory traffic CNN model, which is fast and efficient. The basic concept is to minimize both computational cost and memory access cost at the same time, such that the HarDNet models are 35% faster than ResNet running on GPU comparing to models with the same accuracy (except the two DS models that were designed for comparing with MobileNet).\\'}', metadata={})]", "category": "generic"}
{"question_id": 137, "text": " I need an efficient model for image classification with good accuracy. Provide me with an API that uses LIF neurons.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'SNNMLP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'huawei-noah/Efficient-AI-Backbones\\', model=\\'snnmlp_b\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'snnmlp_b\\', \\'type\\': \\'str\\', \\'description\\': \\'SNNMLP Base model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'huawei-noah/Efficient-AI-Backbones\\', \\'snnmlp_b\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'print(torch.nn.functional.softmax(output[0], dim=0))\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'SNNMLP Base\\', \\'top-1\\': 85.59}}, \\'description\\': \\'SNNMLP incorporates the mechanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNNMLP model achieves 81.9%, 83.3% and 83.6% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively.\\'}', metadata={})]", "category": "generic"}
{"question_id": 138, "text": " As a market research analyst, I want to find a tool to classify different product types using their images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50_cutmix\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50_cutmix\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 + CutMix w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.98\\', \\'top5\\': \\'95.35\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 139, "text": " A media company that works with image recognition is trying to identify an object in an image. Recommend an API that specializes in image recognition.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 140, "text": " Inform me of an API that can help identify famous landmarks from images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 141, "text": " I am working on an image classification project where accuracy is important, and I need a pretrained model that has a lower error rate when classifying images. What model might work for me?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 142, "text": " The New York Times wants to classify some information about Jim Henson. Recommend an API to analyze and classify the text.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Natural Language Processing\\', \\'api_name\\': \\'PyTorch-Transformers\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'huggingface/pytorch-transformers\\')\", \\'api_arguments\\': [\\'pretrained_model_or_path\\', \\'output_attention\\', \\'output_hidden_states\\', \\'config\\', \\'from_tf\\'], \\'python_environment_requirements\\': [\\'tqdm\\', \\'boto3\\', \\'requests\\', \\'regex\\', \\'sentencepiece\\', \\'sacremoses\\'], \\'example_code\\': \\'import torch\\\\ntokenizer = torch.hub.load(\\\\\\'huggingface/pytorch-transformers\\\\\\', \\\\\\'tokenizer\\\\\\', \\\\\\'bert-base-cased\\\\\\')\\\\n\\\\ntext_1 = \"Jim Henson was a puppeteer\"\\\\ntext_2 = \"Who was Jim Henson ?\"\\\\n\\\\nindexed_tokens = tokenizer.encode(text_1, text_2, add_special_tokens=True)\\\\n\\\\nmodel = torch.hub.load(\\\\\\'huggingface/pytorch-transformers\\\\\\', \\\\\\'model\\\\\\', \\\\\\'bert-base-cased\\\\\\')\\\\n\\\\nwith torch.no_grad():\\\\n encoded_layers, _ = model(tokens_tensor, token_type_ids=segments_tensors)\\', \\'performance\\': {\\'dataset\\': [{\\'name\\': \\'MRPC\\', \\'accuracy\\': \\'Not provided\\'}]}, \\'description\\': \\'PyTorch-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP) including BERT, GPT, GPT-2, Transformer-XL, XLNet, XLM, RoBERTa, and DistilBERT. The library provides functionality for tokenization, configuration, and various model architectures for different tasks such as causal language modeling, sequence classification, question answering, and masked language modeling.\\'}', metadata={})]", "category": "generic"}
{"question_id": 143, "text": " Recommend a pretrained API that classifies animals from an image given the photo of the animal.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 144, "text": " I have a picture of my dog and I want to classify its breed. Provide me an API to do this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13_bn\\': {\\'Top-1 error\\': 28.45, \\'Top-5 error\\': 9.63}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 145, "text": " A developer at Pinterest wants to automatically categorize uploaded images based on their content. Provide an API suggestion that can help with this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 146, "text": " A startup is working on a computer vision application supporting autonomous drones. Can you provide an API that can compute the relative depth of an object in a given image?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Hybrid\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Hybrid\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 147, "text": " Imagine you are trying to build podcast transcription for people who are impaired. Get an API to transcribe a sample podcast from Spotify.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 148, "text": " A tourist is planning to take a picture of a beautiful scene but wants to separate the people from the background. Recommend an API to help do this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 149, "text": " I took a photo and I want to detect all the objects in the image. Provide me with an API to do this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 150, "text": " Find an API that can generate new images of various clothing styles in 64x64 resolution using Generative Adversarial Networks.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Generative Adversarial Networks (GANs)\\', \\'api_name\\': \\'PGAN\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/pytorch_GAN_zoo:hub\\', model=\\'PGAN\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'facebookresearch/pytorch_GAN_zoo:hub\\', \\'model\\': \\'PGAN\\', \\'model_name\\': \\'celebAHQ-512\\', \\'pretrained\\': \\'True\\', \\'useGPU\\': \\'use_gpu\\'}, \\'python_environment_requirements\\': \\'Python 3\\', \\'example_code\\': {\\'import\\': \\'import torch\\', \\'use_gpu\\': \\'use_gpu = True if torch.cuda.is_available() else False\\', \\'load_model\\': \"model = torch.hub.load(\\'facebookresearch/pytorch_GAN_zoo:hub\\', \\'PGAN\\', model_name=\\'celebAHQ-512\\', pretrained=True, useGPU=use_gpu)\", \\'build_noise_data\\': \\'noise, _ = model.buildNoiseData(num_images)\\', \\'test\\': \\'generated_images = model.test(noise)\\', \\'plot_images\\': {\\'import_matplotlib\\': \\'import matplotlib.pyplot as plt\\', \\'import_torchvision\\': \\'import torchvision\\', \\'make_grid\\': \\'grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)\\', \\'imshow\\': \\'plt.imshow(grid.permute(1, 2, 0).cpu().numpy())\\', \\'show\\': \\'plt.show()\\'}}, \\'performance\\': {\\'dataset\\': \\'celebA\\', \\'accuracy\\': \\'High-quality celebrity faces\\'}, \\'description\\': \"Progressive Growing of GANs (PGAN) is a method for generating high-resolution images using generative adversarial networks. The model is trained progressively, starting with low-resolution images and gradually increasing the resolution until the desired output is achieved. This implementation is based on the paper by Tero Karras et al., \\'Progressive Growing of GANs for Improved Quality, Stability, and Variation\\'.\"}', metadata={})]", "category": "generic"}
{"question_id": 151, "text": " I am trying to classify an image to find its category. Please give me an API that can identify the content of an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 152, "text": " I would like to convert text to natural sounding speech using Deep Learning. Can you provide me with an API to achieve this?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 153, "text": " Design a system to diagnose diseases from X-Ray images. Recommend an appropriate API for classifying diseases in the X-Ray images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'ResNext\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'resnext50_32x4d\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\'], \\'example_code\\': [\\'import torch\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'resnext50_32x4d\\', pretrained=True)\", \\'model.eval()\\', \"input_image = Image.open(\\'dog.jpg\\')\", \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'resnext50_32x4d\\': {\\'top-1\\': 22.38, \\'top-5\\': 6.3}}}, \\'description\\': \\'ResNext is a next-generation ResNet architecture for image classification. It is more efficient and accurate than the original ResNet. This implementation includes two versions of the model, resnext50_32x4d and resnext101_32x8d, with 50 and 101 layers respectively.\\'}', metadata={})]", "category": "generic"}
{"question_id": 154, "text": " A smartphone company is developing an app that can classify object from a picture. Provide an API that can achieve this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 155, "text": " I want to create an app that recognizes items from pictures taken by users. Can you recommend any machine learning API for this purpose?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MobileNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'mobilenet_v2\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision\\', \\'mobilenet_v2\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 28.12, \\'top-5_error\\': 9.71}}, \\'description\\': \\'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\\'}', metadata={})]", "category": "generic"}
{"question_id": 156, "text": " Recommend an API that can be used for image classification tasks on a dataset of images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 157, "text": " Find out an API that can identify 102 different types of flowers from an image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_resnest50\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_resnest50\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ ResNet50\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'25.6M\\', \\'top1\\': \\'80.67\\', \\'top5\\': \\'95.09\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 158, "text": " Can you recommend an API for image classification which is efficient in terms of computational resources and has decent accuracy?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'EfficientNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\', \\'pretrained\\'], \\'python_environment_requirements\\': [\\'validators\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nefficientnet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_efficientnet_b0\\', pretrained=True)\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\n\\\\nefficientnet.eval().to(device)\\\\n\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\n\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\\\n \\\\nresults = utils.pick_n_best(predictions=output, n=5)\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'IMAGENET\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \\'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 159, "text": " A photography service needs a fast algorithm to recognize objects in their images from the ImageNet dataset out of the box. What API should they use?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 160, "text": " Can you suggest an API for classifying images in my dataset using a model with spiking neural networks?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'ShuffleNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'shufflenet_v2_x1_0\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'torch\\', \\'torchvision\\': \\'torchvision\\', \\'PIL\\': \\'Image\\', \\'urllib\\': \\'urllib\\'}, \\'example_code\\': {\\'import_libraries\\': [\\'import torch\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'import urllib\\'], \\'load_model\\': [\"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'shufflenet_v2_x1_0\\', pretrained=True)\", \\'model.eval()\\'], \\'load_image\\': [\"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'try: urllib.URLopener().retrieve(url, filename)\\', \\'except: urllib.request.urlretrieve(url, filename)\\', \\'input_image = Image.open(filename)\\'], \\'preprocess_image\\': [\\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\'], \\'run_inference\\': [\\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\'], \\'get_probabilities\\': [\\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'top_categories\\': [\\'top5_prob, top5_catid = torch.topk(probabilities, 5)\\', \\'for i in range(top5_prob.size(0)):\\', \\' print(categories[top5_catid[i]], top5_prob[i].item())\\']}, \\'performance\\': {\\'dataset\\': \\'Imagenet\\', \\'accuracy\\': {\\'top-1_error\\': 30.64, \\'top-5_error\\': 11.68}}, \\'description\\': \\'ShuffleNet V2 is an efficient ConvNet optimized for speed and memory, pre-trained on Imagenet. It is designed based on practical guidelines for efficient network design, including speed and accuracy tradeoff.\\'}', metadata={})]", "category": "generic"}
{"question_id": 161, "text": " I am trying to recognize objects in an image using a popular image classification model. Which model should I use?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg16_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg16_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG16 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg16_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg16_bn\\': {\\'Top-1 error\\': 26.63, \\'Top-5 error\\': 8.5}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 162, "text": " I want to create an app to recognize objects in images. Which API is suitable for this task?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 163, "text": " Air Traffic Control needs an image classifier to identify if an image contains an aircraft or not. Suggest an API that would be suitable for this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 164, "text": " A smart fridge wants to identify food items from images taken from its camera. Provide an API to identify the food items.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MEAL_V2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'szq0214/MEAL-V2\\', \\'meal_v2\\', model=\\'mealv2_mobilenetv3_small_100\\', pretrained=True)\", \\'api_arguments\\': {\\'model_name\\': \\'mealv2_mobilenetv3_small_100\\'}, \\'python_environment_requirements\\': \\'!pip install timm\\', \\'example_code\\': \"import torch\\\\nfrom PIL import Image\\\\nfrom torchvision import transforms\\\\n\\\\nmodel = torch.hub.load(\\'szq0214/MEAL-V2\\',\\'meal_v2\\', \\'mealv2_resnest50_cutmix\\', pretrained=True)\\\\nmodel.eval()\\\\n\\\\ninput_image = Image.open(\\'dog.jpg\\')\\\\npreprocess = transforms.Compose([\\\\n transforms.Resize(256),\\\\n transforms.CenterCrop(224),\\\\n transforms.ToTensor(),\\\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\\\n])\\\\ninput_tensor = preprocess(input_image)\\\\ninput_batch = input_tensor.unsqueeze(0)\\\\n\\\\nif torch.cuda.is_available():\\\\n input_batch = input_batch.to(\\'cuda\\')\\\\n model.to(\\'cuda\\')\\\\n\\\\nwith torch.no_grad():\\\\n output = model(input_batch)\\\\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\\\\nprint(probabilities)\", \\'performance\\': [{\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'model\\': \\'MEAL-V2 w/ MobileNet V3-Small 1.0\\', \\'resolution\\': \\'224\\', \\'parameters\\': \\'2.54M\\', \\'top1\\': \\'69.65\\', \\'top5\\': \\'88.71\\'}}], \\'description\\': \\'MEAL V2 models are from the MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks paper. The method is based on ensemble knowledge distillation via discriminators, and it achieves state-of-the-art results without using common tricks such as architecture modification, outside training data, autoaug/randaug, cosine learning rate, mixup/cutmix training, or label smoothing.\\'}', metadata={})]", "category": "generic"}
{"question_id": 165, "text": " I want to count how many people are present in a room using an image. Tell me an API that can do this task.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'ResNext WSL\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'facebookresearch/WSL-Images\\', model=\\'resnext101_32x16d_wsl\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'resnext101_32x16d_wsl\\', \\'type\\': \\'str\\', \\'description\\': \\'ResNeXt-101 32x16d WSL model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'facebookresearch/WSL-Images\\', \\'resnext101_32x16d_wsl\\')\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'print(output[0])\\', \\'print(torch.nn.functional.softmax(output[0], dim=0))\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'ResNeXt-101 32x16d\\': {\\'Top-1 Acc.\\': \\'84.2\\', \\'Top-5 Acc.\\': \\'97.2\\'}}}, \\'description\\': \\'The provided ResNeXt models are pre-trained in weakly-supervised fashion on 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. The models significantly improve the training accuracy on ImageNet compared to training from scratch. They achieve state-of-the-art accuracy of 85.4% on ImageNet with the ResNext-101 32x48d model.\\'}', metadata={})]", "category": "generic"}
{"question_id": 166, "text": " I am developing a website that can predict the content of an image based on its URL. What API would you recommend with a code example?\\n###Input: {\\\"image_url\\\": \\\"https://example.com/image.jpg\\\"}\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 167, "text": " A wildlife photographer wants to classify animals in images taken during a safari. Provide me with an API that can help classify these animals.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 168, "text": " I want to use my camera app to identify objects that I point it to. What API would you recommend?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 169, "text": " I am building an image classification model and want to achieve a high accuracy. Which API should I use?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 170, "text": " A photographer at a film studio wants to find the relative depth from a single image. Recommend an API that can compute relative depth from an input image.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Hybrid\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Hybrid\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 171, "text": " A bird watching society is developing an app that can identify birds in a picture. Provide a suitable API that can be used for classifying birds from images.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13_bn\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13_bn\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model with batch normalization\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13_bn\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13_bn\\': {\\'Top-1 error\\': 28.45, \\'Top-5 error\\': 9.63}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 172, "text": " Provide an API recommendation for a call center which wants to convert customer voice calls into text.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 173, "text": " Provide me with an API that can tackle city-scape segmentation in autonomous driving application.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Traffic Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'HybridNets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'datvuthanh/hybridnets\\', model=\\'hybridnets\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'Python>=3.7, PyTorch>=1.10\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'datvuthanh/hybridnets\\', \\'hybridnets\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,384)\\\\nfeatures, regression, classification, anchors, segmentation = model(img)\", \\'performance\\': {\\'dataset\\': [{\\'name\\': \\'BDD100K\\', \\'accuracy\\': {\\'Traffic Object Detection\\': {\\'Recall (%)\\': 92.8, \\'mAP@0.5 (%)\\': 77.3}, \\'Drivable Area Segmentation\\': {\\'Drivable mIoU (%)\\': 90.5}, \\'Lane Line Detection\\': {\\'Accuracy (%)\\': 85.4, \\'Lane Line IoU (%)\\': 31.6}}}]}, \\'description\\': \\'HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 174, "text": " I need an API to extract features from a collection of photographs taken at the 2022 Olympics.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 175, "text": " An E-commerce manager wants to develop an image classification system for their products. They need a powerful pre-trained model as a starting point. Recommend an API for this purpose.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 176, "text": " I need an API to classify images with known objects. Suggest a suitable model that can do this.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}
{"question_id": 177, "text": " A delivery company wants to recognize if a package is damaged during shipment. Propose an API that can classify images into damaged and undamaged packages.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 178, "text": " An image recognition app needs to identify objects from the images it captures. Suggest an API which is optimized for GPUs.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Single Shot MultiBox Detector\\', \\'api_name\\': \\'SSD\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_ssd\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\'], \\'python_environment_requirements\\': [\\'numpy\\', \\'scipy\\', \\'scikit-image\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nssd_model = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_ssd\\')\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_ssd_processing_utils\\')\\\\n\\\\nssd_model.to(\\'cuda\\')\\\\nssd_model.eval()\\\\n\\\\ninputs = [utils.prepare_input(uri) for uri in uris]\\\\ntensor = utils.prepare_tensor(inputs)\\\\n\\\\nwith torch.no_grad():\\\\n detections_batch = ssd_model(tensor)\\\\n\\\\nresults_per_input = utils.decode_results(detections_batch)\\\\nbest_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'COCO\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \"The SSD (Single Shot MultiBox Detector) model is an object detection model based on the paper \\'SSD: Single Shot MultiBox Detector\\'. It uses a deep neural network for detecting objects in images. This implementation replaces the obsolete VGG model backbone with the more modern ResNet-50 model. The SSD model is trained on the COCO dataset and can be used to detect objects in images with high accuracy and efficiency.\"}', metadata={})]", "category": "generic"}
{"question_id": 179, "text": " Show me an API that provides easy to use neural networks for classifying different types of wildlife on mobile platforms.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'MobileNet v2\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'mobilenet_v2\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\', \\'PIL\\', \\'urllib\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision\\', \\'mobilenet_v2\\', pretrained=True)\", \\'model.eval()\\', \\'from PIL import Image\\', \\'from torchvision import transforms\\', \\'input_image = Image.open(filename)\\', \\'preprocess = transforms.Compose([\\', \\' transforms.Resize(256),\\', \\' transforms.CenterCrop(224),\\', \\' transforms.ToTensor(),\\', \\' transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\', \\'])\\', \\'input_tensor = preprocess(input_image)\\', \\'input_batch = input_tensor.unsqueeze(0)\\', \\'if torch.cuda.is_available():\\', \" input_batch = input_batch.to(\\'cuda\\')\", \" model.to(\\'cuda\\')\", \\'with torch.no_grad():\\', \\' output = model(input_batch)\\', \\'probabilities = torch.nn.functional.softmax(output[0], dim=0)\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 28.12, \\'top-5_error\\': 9.71}}, \\'description\\': \\'The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power.\\'}', metadata={})]", "category": "generic"}
{"question_id": 180, "text": " Recommend an API for identifying defective parts in a manufacturing assembly line based on images taken by an inspection system.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Computing relative depth from a single image\\', \\'api_name\\': \\'MiDaS\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'intel-isl/MiDaS\\', model=\\'DPT_Large\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'intel-isl/MiDaS\\', \\'model\\': \\'model_type\\'}, \\'python_environment_requirements\\': \\'pip install timm\\', \\'example_code\\': [\\'import cv2\\', \\'import torch\\', \\'import urllib.request\\', \\'import matplotlib.pyplot as plt\\', \"url, filename = (\\'https://github.com/pytorch/hub/raw/master/images/dog.jpg\\', \\'dog.jpg\\')\", \\'urllib.request.urlretrieve(url, filename)\\', \"model_type = \\'DPT_Large\\'\", \"midas = torch.hub.load(\\'intel-isl/MiDaS\\', \\'DPT_Large\\')\", \"device = torch.device(\\'cuda\\') if torch.cuda.is_available() else torch.device(\\'cpu\\')\", \\'midas.to(device)\\', \\'midas.eval()\\', \"midas_transforms = torch.hub.load(\\'intel-isl/MiDaS\\', \\'transforms\\')\", \"if model_type == \\'DPT_Large\\' or model_type == \\'DPT_Hybrid\\':\", \\' transform = midas_transforms.dpt_transform\\', \\'else:\\', \\' transform = midas_transforms.small_transform\\', \\'img = cv2.imread(filename)\\', \\'img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\', \\'input_batch = transform(img).to(device)\\', \\'with torch.no_grad():\\', \\' prediction = midas(input_batch)\\', \\'prediction = torch.nn.functional.interpolate(\\', \\' prediction.unsqueeze(1),\\', \\' size=img.shape[:2],\\', \" mode=\\'bicubic\\',\", \\' align_corners=False,\\', \\').squeeze()\\', \\'output = prediction.cpu().numpy()\\', \\'plt.imshow(output)\\', \\'plt.show()\\'], \\'performance\\': {\\'dataset\\': \\'10 distinct datasets\\', \\'accuracy\\': \\'Multi-objective optimization\\'}, \\'description\\': \\'MiDaS computes relative inverse depth from a single image. The repository provides multiple models that cover different use cases ranging from a small, high-speed model to a very large model that provide the highest accuracy. The models have been trained on 10 distinct datasets using multi-objective optimization to ensure high quality on a wide range of inputs.\\'}', metadata={})]", "category": "generic"}
{"question_id": 181, "text": " Identify an image classification API that can be used to determine if an object is a car, a bike, or a pedestrian.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Object Detection\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Object Detection, Drivable Area Segmentation, Lane Detection\\', \\'api_name\\': \\'YOLOP\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'hustvl/yolop\\', model=\\'yolop\\', pretrained=True)\", \\'api_arguments\\': \\'pretrained\\', \\'python_environment_requirements\\': \\'pip install -qr https://github.com/hustvl/YOLOP/blob/main/requirements.txt\\', \\'example_code\\': \"import torch\\\\nmodel = torch.hub.load(\\'hustvl/yolop\\', \\'yolop\\', pretrained=True)\\\\nimg = torch.randn(1,3,640,640)\\\\ndet_out, da_seg_out,ll_seg_out = model(img)\", \\'performance\\': {\\'dataset\\': \\'BDD100K\\', \\'accuracy\\': {\\'Object Detection\\': {\\'Recall(%)\\': 89.2, \\'mAP50(%)\\': 76.5, \\'Speed(fps)\\': 41}, \\'Drivable Area Segmentation\\': {\\'mIOU(%)\\': 91.5, \\'Speed(fps)\\': 41}, \\'Lane Detection\\': {\\'mIOU(%)\\': 70.5, \\'IOU(%)\\': 26.2}}}, \\'description\\': \\'YOLOP is an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection. And it is also the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the BDD100K dataset.\\'}', metadata={})]", "category": "generic"}
{"question_id": 182, "text": " I need an API to classify images efficiently without sacrificing too much accuracy. Can you provide me with one?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 183, "text": " To save the environment, a student wants to evaluate how green his schools area is. Tell me an AI API which can classify the images of plants in his environment and tell the name of the plants.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'AlexNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'alexnet\\', pretrained=True)\", \\'api_arguments\\': {\\'pretrained\\': \\'True\\'}, \\'python_environment_requirements\\': {\\'torch\\': \\'>=1.9.0\\', \\'torchvision\\': \\'>=0.10.0\\'}, \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'alexnet\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'top-1_error\\': 43.45, \\'top-5_error\\': 20.91}}, \\'description\\': \\'AlexNet is a deep convolutional neural network that achieved a top-5 error of 15.3% in the 2012 ImageNet Large Scale Visual Recognition Challenge. The main contribution of the original paper was the depth of the model, which was computationally expensive but made feasible through the use of GPUs during training. The pretrained AlexNet model in PyTorch can be used for image classification tasks.\\'}', metadata={})]", "category": "generic"}
{"question_id": 184, "text": " I need an efficient API to classify images on multiple edge devices with different resource constraints. Suggest one for me.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Classification\\', \\'api_name\\': \\'EfficientNet\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'NVIDIA/DeepLearningExamples:torchhub\\', model=\\'nvidia_efficientnet_b0\\', pretrained=True)\", \\'api_arguments\\': [\\'model_name\\', \\'pretrained\\'], \\'python_environment_requirements\\': [\\'validators\\', \\'matplotlib\\'], \\'example_code\\': \"import torch\\\\n\\\\nefficientnet = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_efficientnet_b0\\', pretrained=True)\\\\nutils = torch.hub.load(\\'NVIDIA/DeepLearningExamples:torchhub\\', \\'nvidia_convnets_processing_utils\\')\\\\n\\\\nefficientnet.eval().to(device)\\\\n\\\\nbatch = torch.cat([utils.prepare_input_from_uri(uri) for uri in uris]).to(device)\\\\n\\\\nwith torch.no_grad():\\\\n output = torch.nn.functional.softmax(efficientnet(batch), dim=1)\\\\n \\\\nresults = utils.pick_n_best(predictions=output, n=5)\", \\'performance\\': {\\'dataset\\': {\\'name\\': \\'IMAGENET\\', \\'accuracy\\': \\'Not provided\\'}}, \\'description\\': \\'EfficientNet is a family of image classification models that achieve state-of-the-art accuracy while being smaller and faster. The models are trained with mixed precision using Tensor Cores on the NVIDIA Volta and Ampere GPU architectures. The EfficientNet models include EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0, and EfficientNet-WideSE-B4. The WideSE models use wider Squeeze-and-Excitation layers than the original EfficientNet models, resulting in slightly better accuracy.\\'}', metadata={})]", "category": "generic"}
{"question_id": 185, "text": " I want my app to be able to read aloud the text for audiobooks. Can you suggest me an API for converting text to speech?\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Text-To-Speech\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Text-To-Speech\\', \\'api_name\\': \\'Silero Text-To-Speech Models\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', pretrained=True)\", \\'api_arguments\\': {\\'repo_or_dir\\': \\'snakers4/silero-models\\', \\'model\\': \\'silero_tts\\', \\'language\\': \\'language\\', \\'speaker\\': \\'speaker\\'}, \\'python_environment_requirements\\': [\\'pip install -q torchaudio omegaconf\\'], \\'example_code\\': \"import torch\\\\nlanguage = \\'en\\'\\\\nspeaker = \\'lj_16khz\\'\\\\ndevice = torch.device(\\'cpu\\')\\\\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir=\\'snakers4/silero-models\\', model=\\'silero_tts\\', language=language, speaker=speaker)\\\\nmodel = model.to(device)\\\\naudio = apply_tts(texts=[example_text], model=model, sample_rate=sample_rate, symbols=symbols, device=device)\", \\'performance\\': {\\'dataset\\': [{\\'language\\': \\'Russian\\', \\'speakers\\': 6}, {\\'language\\': \\'English\\', \\'speakers\\': 1}, {\\'language\\': \\'German\\', \\'speakers\\': 1}, {\\'language\\': \\'Spanish\\', \\'speakers\\': 1}, {\\'language\\': \\'French\\', \\'speakers\\': 1}], \\'accuracy\\': \\'High throughput on slow hardware. Decent performance on one CPU thread\\'}, \\'description\\': \\'Silero Text-To-Speech models provide enterprise grade TTS in a compact form-factor for several commonly spoken languages. They offer one-line usage, naturally sounding speech, no GPU or training required, minimalism and lack of dependencies, a library of voices in many languages, support for 16kHz and 8kHz out of the box.\\'}', metadata={})]", "category": "generic"}
{"question_id": 186, "text": " An app wants to identify dog breeds from images taken by users. Recommend an API that can classify the dog breed given a photo of a dog.\\n \n Use this API documentation for reference: [Document(page_content='{\\'domain\\': \\'Classification\\', \\'framework\\': \\'PyTorch\\', \\'functionality\\': \\'Image Recognition\\', \\'api_name\\': \\'vgg-nets\\', \\'api_call\\': \"torch.hub.load(repo_or_dir=\\'pytorch/vision\\', model=\\'vgg13\\', pretrained=True)\", \\'api_arguments\\': [{\\'name\\': \\'vgg13\\', \\'type\\': \\'str\\', \\'description\\': \\'VGG13 model\\'}], \\'python_environment_requirements\\': [\\'torch\\', \\'torchvision\\'], \\'example_code\\': [\\'import torch\\', \"model = torch.hub.load(\\'pytorch/vision:v0.10.0\\', \\'vgg13\\', pretrained=True)\", \\'model.eval()\\'], \\'performance\\': {\\'dataset\\': \\'ImageNet\\', \\'accuracy\\': {\\'vgg13\\': {\\'Top-1 error\\': 30.07, \\'Top-5 error\\': 10.75}}}, \\'description\\': \\'vgg-nets are award-winning ConvNets from the 2014 Imagenet ILSVRC challenge. They are used for large-scale image recognition tasks. The available models are vgg11, vgg11_bn, vgg13, vgg13_bn,vgg16, vgg16_bn, vgg19, and vgg19_bn.\\'}', metadata={})]", "category": "generic"}