torchhub_train_openai.jsonl

torchhub_train_openai.jsonl

{"prompt":"I need an API that can estimate the relative depth of objects from a single image. The output should be an inverse depth map.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MiDaS model for estimating relative depth from a single image, which outputs an inverse depth map.\", 'code': 'import torch\nmodel = torch.hub.load(\"intel-isl\/MiDaS\", \"DPT_Hybrid\", pretrained=True)'}###"}
{"prompt":"I need an API to detect different types of vehicles, people, and lanes in an image captured from a camera mounted on a self-driving car.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the YOLOP model from PyTorch Hub with pre-trained weights optimized for object detection, drivable area segmentation, and lane detection in autonomous driving scenarios.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"\"I need to calculate the single-image depth map of a captured image. Present an API that can perform this complex task.\"\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MiDaS DPT_Large model from PyTorch Hub to compute relative inverse depth from a single image, giving you a depth map.\", 'code': 'import torch\nmodel = torch.hub.load(\"intel-isl\/MiDaS\", \"DPT_Large\", pretrained=True)'}###"}
{"prompt":"Imagine an advertisement company wants to analyze various video streams for the presence of specific activities or actions. Provide them with an API that can perform this video classification task.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained 3D ResNet model, capable of classifying video streams into various action categories, from PyTorch Hub.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"Recommend an API for estimating relative depth from a single image for a self-driving vehicle startup.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which computes relative depth from a single image. This model can be helpful for self-driving vehicles to estimate depth from their cameras.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"I work for Tesla, and I need a ready-to-use model for detecting and classifying cars, pedestrians, lanes, and drivable area in an image.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained HybridNets model for object detection from PyTorch Hub, which specializes in detecting and classifying cars, pedestrians, lanes, and drivable areas.', 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"A student needs to classify bird species for their science project. Write an API to help identify the bird species given a photo of a bird.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NTS-Net model for fine-grained bird species classification from PyTorch Hub, which can identify the bird species given an input image.', 'code': \"import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\"}###"}
{"prompt":"Help me find an API to convert spoken language in a recorded audio file into text.\n\n###\n\n","completion":" {'domain': 'Speech-to-Text', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_stt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Speech-To-Text model from PyTorch Hub, which can convert a given spoken language in a recorded audio file into text.', 'code': 'import torch\nmodel, _, _ = torch.hub.load('snakers4\/silero-models', 'silero_stt', language='en', device=torch.device('cpu'))'}###"}
{"prompt":"We want to convert text to audio for one of our applications. What API can provide that functionality?\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_tts', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Text-To-Speech model from PyTorch Hub to convert text into high-quality audio.', 'code': \"import torch\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_tts', language='en', speaker='lj_16khz')\"}###"}
{"prompt":"Devise an API used to separate objects from the background in an image.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet-50 backbone from PyTorch Hub to perform semantic segmentation, allowing you to separate objects from the background in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"Please suggest a powerful API to carry out semantic segmentation in a given image.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the DeepLabV3 semantic segmentation model with a MobileNet-V3 large backbone from PyTorch Hub, pretrained on a subset of COCO train2017 dataset.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\"}###"}
{"prompt":"We are developing a voice assistant that needs to detect when a human is speaking. Suggest an API to detect human speech in an audio file.\n\n###\n\n","completion":" {'domain': 'Voice Activity Detection', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero VAD from PyTorch Hub, a pretrained Voice Activity Detector optimized for performance on 1 CPU thread that provides an enterprise-grade alternative to WebRTC.', 'code': 'import torch\nmodel, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)'}###"}
{"prompt":"Design an API for segmenting the objects in an image with the highest accuracy.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the DeepLabV3+ model with a ResNet-101 backbone from PyTorch Hub, which is pretrained on COCO val2017 for semantic segmentation, to segment objects in an image with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)'}###"}
{"prompt":"We need an API for a security system that can detect objects in real-time. Recommend a solution.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5 model from PyTorch Hub for real-time object detection in your security system.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"NASA is looking to classify celestial images. They need a machine learning API that can identify objects in images. Recommend one.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model for image classification from PyTorch Hub to classify celestial objects in images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)'}###"}
{"prompt":"I want to determine what objects are in an image file. Find me a model API that can classify the objects in the image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNext WSL model from PyTorch Hub that has been trained on ImageNet to recognize various objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"Find me an API for classifying different objects in images with high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNeXt-101 32x16d WSL model from PyTorch Hub for classifying various objects in images with high accuracy.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"I need an API that can create random images of clothes, like shirts, pants or dresses, any ideas?\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained DCGAN model from PyTorch Hub, capable of generating random images of clothing items like shirts, pants, and dresses.\", 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"I am a researcher working on a computer vision project and need a cutting-edge pre-trained image classification API. What do you suggest?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use the ResNext WSL model from PyTorch Hub, which is pretrained and fine-tuned on large-scale datasets, achieving state-of-the-art accuracy on ImageNet.\", 'code': \"import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\"}###"}
{"prompt":"A professional radiologist is looking to process an MRI dataset to isolate abnormalities. Help me find an API that can segment the abnormal regions within brain MRI images.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Locate and load the pretrained U-Net model for brain MRI segmentation from PyTorch Hub, designed to identify and segment abnormal regions in brain MRI images.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"Recommend me an API to classify images with top-notch accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ResNet50 v1.5 model from PyTorch Hub, which is a modified version of the original ResNet50 with improved accuracy for image classification.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)'}###"}
{"prompt":"Help me classify images of cats and dogs, by recommending an API to do that.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt101-32x4d model from PyTorch Hub provided by NVIDIA. It can be fine-tuned to classify images of cats and dogs.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\"}###"}
{"prompt":"I need an API that can be used for image classification tasks with a model trained on ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained AlexNet model, trained on the ImageNet dataset, from PyTorch Hub for image classification tasks.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\"}###"}
{"prompt":"An app developer from San Francisco wants to classify images based on their content. Tell me an API that can classify images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101 model from PyTorch Hub to classify images into different categories based on their content.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"Identify the objects in an image using a dense convolutional network. Recommend an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-121 model from PyTorch Hub for object classification in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"I'm the founder of Dobble, an AI company. We are building a virtual assistant and looking for an API to convert text to speech. Can you provide one?\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained WaveGlow model from PyTorch Hub for text-to-speech synthesis, which can be combined with the Tacotron 2 model to produce natural-sounding speech from text.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_waveglow\", pretrained=True)'}###"}
{"prompt":"Show me an API that can handle textual classification like sentiment analysis, emotion recognition or sarcasm detection.\n\n###\n\n","completion":" {'domain': 'Text Classification', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained RoBERTa model from PyTorch Hub, which can be fine-tuned for various textual classification tasks, such as sentiment analysis, emotion recognition, and sarcasm detection.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)'}###"}
{"prompt":"Recommend a Python API to perform semantic segmentation on an image.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load(\"pytorch\/vision\", \"fcn_resnet101\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the FCN-ResNet101 pretrained model from PyTorch Hub to perform semantic segmentation on images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"fcn_resnet101\", pretrained=True)'}###"}
{"prompt":"I am working on a computer vision app and I need to classify objects in the images. Which API can help me classify objects with high accuracy?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-201 model from PyTorch Hub, which is a dense convolutional network with high accuracy in object classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"We need an API that can be used to create an efficient image classifier for differentiating among various dog breeds in a mobile app.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-39DS model from PyTorch Hub, which is fast and efficient, suitable for a mobile app to classify various dog breeds.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"Recommend an image classification API that requires low memory and runs faster on GPUs to classify user-uploaded photos.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68 model from PyTorch Hub for its low memory usage and faster speed on GPUs, making it suitable for classifying user-uploaded images.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"Identify an API for a clothing e-commerce website that classifies an image into different categories such as tops, shoes, dresses, etc.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet for image classification from PyTorch Hub, which can be fine-tuned for classifying clothing items such as tops, shoes, dresses, and more.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)'}###"}
{"prompt":"I am working on a project that needs a lightweight image classification solution. Suggest me an API to use.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'GhostNet is a lightweight and efficient image classification solution, available in PyTorch Hub. It generates more features from cheap operations, providing a balance between speed and accuracy.', 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\"}###"}
{"prompt":"I am a computer science researcher interested in selecting an efficient image classification model. Can you recommend me an API that would be suitable for that purpose?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the HarDNet-85 model from PyTorch Hub for efficient image classification as it minimizes both computational cost and memory access cost.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"A company is developing a machine-learning based software to categorize their products based on the photos they take. Provide an API that can help them classify their images and name the image types.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a model for image classification from PyTorch Hub, which can be fine-tuned for categorizing product images.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"A machine learning engineer is working on generalization for classification tasks in different domains. Suggest an API that is suitable for this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net (resnet101_ibn_a) model from PyTorch Hub, which is designed for domain and appearance invariance, making it suitable for cross-domain classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"We want our AI powered traffic camera to classify different types of vehicles with high accuracy. Provide an API suggestion for this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt-101-IBN-a model for object classification from PyTorch Hub, which can classify different types of vehicles with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"I'm working on a project that needs a deep learning model to identify similar objects across different domains. Suggest me an API that can serve the purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SE-ResNet-101-IBN-a model from PyTorch Hub, an IBN-Net model that unifies instance normalization and batch normalization, suitable for cross-domain object identification.\", 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"A new ecommerce store wants to automatically categorize images of products into different classes. Recommend an API that can perform image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model for image classification from PyTorch Hub, which can automatically categorize images of products into different classes.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"An e-commerce company is building an AI to classify pictures of products into categories automatically. Recommend an API to help in classifying images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-50 model from PyTorch Hub for high-performance image classification, which can be fine-tuned for the specific task of classifying e-commerce product images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"A developer is trying to build an image classifier. Provide an API capable of classifying images and telling what objects are present.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Inception_v3 model from PyTorch Hub for image classification, which can predict objects present in a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)'}###"}
{"prompt":"Is there an API to implement ResNeSt for image classification purposes?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-200 model from PyTorch Hub for image classification.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)'}###"}
{"prompt":"In order to create classifiers for popular recipes from the internet, I would like to locate an API that can efficiently classify a given food image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt269 model from PyTorch Hub, which can be fine-tuned for food image classification given an image of a recipe.', 'code': \"import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\"}###"}
{"prompt":"Recommend me an API to classify images in a mobile app for users that sorts their images in predefined categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model optimized for mobile devices from PyTorch Hub, which can be fine-tuned for image classification in a mobile app.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"I need to classify the content of an image using a pretrained model optimized for GPU. Show me an API that can do that.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model from PyTorch Hub, which is optimized for GPU and can be used to classify images.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"I need an API for classifying objects in mobile application. What is the best API for classifying objects with mobile devices?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ProxylessNAS model from PyTorch Hub, which specializes in object classification for mobile devices and offers significant performance optimization.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"A photographer needs an AI-based solution to help classify a variety of images. Recommend an appropriate API that can classify an image into one of 1000 categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model from PyTorch Hub for image classification, which can classify images into one of 1000 categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"I have a large image dataset in need of image classification using deep residual networks. Can you recommend an API that can achieve this for me?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet model from PyTorch Hub for deep residual image classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"I am developing an app that can recognize diverse objects in pictures. Identify an API that can help me achieve this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet101 model from PyTorch Hub, which can classify images into diverse object categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)'}###"}
{"prompt":"I am having trouble with my tomato plants and want some help figuring out what's wrong with them. I need an AI that can spot diseases in plants given an image.\n\n###\n\n","completion":" {'domain': 'Agriculture', 'api_call': 'model = torch.hub.load(repo_or_dir='pytorch\/vision', model='resnet50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load a pretrained ResNet50 model from PyTorch Hub that can be fine-tuned for plant disease classification given an image of a plant.', 'code': 'import torch\nmodel = torch.hub.load(repo_or_dir='pytorch\/vision', model='resnet50', pretrained=True)'}###"}
{"prompt":"Which API should I use to build a tomato classification app that detects diseases in tomato plant leaves?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use PyTorch's ResNet152 pre-trained model, which can be fine-tuned for the tomato leaf disease classification application.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\"}###"}
{"prompt":"I want to categorize a given image into a certain class like animals or objects. Identify an API that can do that.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext50_32x4d model from PyTorch Hub to categorize images into various classes such as animals or objects.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\"}###"}
{"prompt":"I want to sort images of animals into appropriate categories. What API should I use?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext image classification model from PyTorch Hub, which can be fine-tuned to classify images of animals into appropriate categories.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"Provide an API for classifying the objects present in an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SNNMLP model for image classification from PyTorch Hub, capable of classifying objects in images based on the ImageNet dataset.\", 'code': 'import torch\nmodel = torch.hub.load(\"huawei-noah\/Efficient-AI-Backbones\", \"snnmlp_b\", pretrained=True)'}###"}
{"prompt":"I want an API that can classify images and tell me what the images are.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP model from PyTorch Hub with pretrained weights for image classification. It can identify the content in the images.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"Find me a pre-trained API to identify objects in real-time, taking into account a database of more than 100 classes from ImageNet.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Tiny model from PyTorch Hub trained on ImageNet, which contains more than 100 classes for object classification.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)'}###"}
{"prompt":"As a biologist, I need to identify the species in a photo. Which API should I use to classify images into different categories?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet v1.0 model from PyTorch Hub. This model can be fine-tuned for species classification.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\"}###"}
{"prompt":"I need a lightweight API to classify images based on a training set of images from my new mobile app.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ShuffleNet v2 model from PyTorch Hub. It is an efficient image classification model that is lightweight and suitable for usage in mobile apps.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)'}###"}
{"prompt":"What is an efficient deep learning model for categorizing images into object classes with fewer parameters?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet 1.1 model for image classification from PyTorch Hub. It is efficient in terms of the number of parameters and model size, achieving AlexNet-level accuracy with 50x fewer parameters.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)'}###"}
{"prompt":"Recommend a well-tested API to identify objects in a digital photograph for my photo gallery website.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG-11 model for image recognition from PyTorch Hub to identify objects in a digital photograph.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"I want an API that allows me to recognize and classify different breeds of dogs from images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG11 model with batch normalization from PyTorch Hub to classify different breeds of dogs from images. Fine-tune the model on a dataset of dog breed images.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"I am a software engineer and I am trying to create an image recognition system for my latest project. Find an API that can tell me the category of the objects in a provided image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the VGG13 model with batch normalization from PyTorch Hub, which can be used for image recognition tasks to identify objects in a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)'}###"}
{"prompt":"Propose an API to identify what is in a photo of a bird in order to label it with the correct species.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub to recognize bird species in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"Tell me the best API to be used on a security camera to classify vehicles and details about them given an image\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model from PyTorch Hub to classify vehicles and their details from a given image captured by a security camera.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)'}###"}
{"prompt":"An e-commerce website looking for APIs to create image classifiers for their products. Offer a solution based on the VGG model.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model with batch normalization from PyTorch Hub, which can be fine-tuned for product image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)'}###"}
{"prompt":"Recommending the best machine learning models or APIs that is good for classifying a dataset of images with over 1000 categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub, suitable for classifying large datasets with over 1000 categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"An e-commerce platform wants to categorize their items by image. Tell me an API that can do this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-50-2 model for image classification from PyTorch Hub, which can be used to categorize items based on their images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)'}###"}
{"prompt":"Suggest a neural network that can effectively identify different types of birds in high-resolution images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet 101 model from PyTorch Hub, which can be fine-tuned to identify different types of birds in high-resolution images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"I want to build a video filter app that can separate video clips by different activities. Can you suggest an API that can classify videos into different categories based on the activities happening in the video?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SlowFast model for video classification from PyTorch Hub, which can classify video clips into different activity categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)'}###"}
{"prompt":"What's the best API for automatically detecting sports or activities types in a video clip for an application?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained X3D model for video classification from PyTorch Hub, which can classify video clips into different sports or activities types based on the Kinetics 400 dataset.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"A mobile app company wants to build an object classification solution for their Android app. Give me an API that can classify objects in images with low latency and high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Once-For-All (OFA) network from PyTorch Hub, a family of efficient neural networks designed for object classification in images, with low latency and high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"I would like to build a tool that can produce human-like text given an input prompt. Recommend an API that can help me construct realistic sentences.\n\n###\n\n","completion":" {'domain': 'Natural Language Processing', 'api_call': 'model = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'model\\', \\'gpt2\\')', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GPT-2 model from Hugging Face\\'s PyTorch-Transformers library to generate human-like text given an input prompt.', 'code': 'import torch\nmodel = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'model\\', \\'gpt2\\')'}###"}
{"prompt":"A wildlife photographer needs to classify his collection of animal images. Which API can be used to assist the photographer in image classification?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained semi-supervised ResNet-18 model from PyTorch Hub for classifying images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"I work at a small company that creates a deep learning-based smartphone app. I need a model to classify images super fast and with high performance. Guide me to the right API.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Use the GPUNet model from NVIDIA that is optimized for deep learning image classification tasks and runs efficiently on GPU.', 'code': \"import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\ngpunet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\"}###"}
{"prompt":"Recommend an API for translating English text to French text.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': \"Load the Transformer (NMT) model for English-to-French translation from PyTorch Hub, which uses state-of-the-art neural machine translation techniques.\", 'code': \"import torch\nen2fr = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\"}###"}
{"prompt":"Tell me an API for image classification with high accuracy and less computation resources.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet model from PyTorch Hub, which achieves high accuracy while being small and fast, making it suitable for efficient image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)'}###"}
{"prompt":"A social media company wants to detect if an image uploaded by users contains any inappropriate content. Recommend an API for this.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Single Shot MultiBox Detector (SSD) model from PyTorch Hub, which can be used to detect objects in images with high accuracy and efficiency.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"Create a program to identify the species of a bird from an image taken from a bird watching trip in north America.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with ResNeSt50 backbone from PyTorch Hub. You can fine-tune this model for bird species classification using an annotated dataset of bird images.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I run an online fashion store, and I'm looking for an AI model that can categorize images of clothing items into their respective categories. Suggest a suitable API for me to use.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 ResNet50 model from PyTorch Hub, which can be fine-tuned for fashion item classification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"What API do you recommend to discriminate between different breeds of dogs using only the color of the dogs?'\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3-Small 1.0 architecture from PyTorch Hub. This model can be fine-tuned for dog breed classification based on their color.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\"}###"}
{"prompt":"Present an API to determine what plant is in a photo from a database containing 100,000 images without the use of common tricks.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model from PyTorch Hub, which can be fine-tuned for plant classification without common tricks. The model yields state-of-the-art results on large databases.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)\"}###"}
{"prompt":"I am working on a project to classify images into different classes. Give me a API that has been pre-trained and can classify images across a wide range of categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3-Small 0.75 architecture from PyTorch Hub, which can classify images across a wide range of categories.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"I need an API that does Image Classification, which is pretrained on ImageNet dataset and has high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2','mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 model based on MobileNet V3-Large 1.0, which has an accuracy of 76.92% (Top-1) and 93.32% (Top-5) on the ImageNet dataset.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"How can we convert spoken text \"The weather is nice today. Should we go for a walk?\" into speech using a deep learning model?, 'Input': 'The weather is nice today. Should we go for a walk?'\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"tacotron2 = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Use the NVIDIA Tacotron 2 model to convert the spoken text into mel spectrograms and then use the WaveGlow model to generate speech from these mel spectrograms.', 'code': 'import torch\ntacotron2 = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_tacotron2\", model_math=\"fp16\") \nutils = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_tts_utils\") \ngpu = \"cuda\" if torch.cuda.is_available() else \"cpu\"\ntacotron2 = tacotron2.to(gpu).eval()\n\ntext = \"The weather is nice today. Should we go for a walk?\"\nsequences, lengths = utils.prepare_input_sequence([text])\nwith torch.no_grad():\n    mel, _, _ = tacotron2.infer(sequences.to(gpu), lengths)\n    audio = waveglow.infer(mel)'\n}###"}
{"prompt":"FastEVs is developing an electric autonomous vehicle and needs an API to perceive traffic objects and detect drivable areas in real-time. Recommend a suitable API.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HybridNets model from PyTorch Hub to detect traffic objects, drivable areas, and lanes in real-time for autonomous vehicles.', 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"Develop an application that classifies actions in sports videos. Recommend a suitable API to help analyze the videos and understand the actions performed.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained 3D ResNet model from PyTorch Hub, which is trained on the Kinetics 400 dataset and can be used to classify sports actions performed in videos.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"A startup is looking to build a drone security system for detecting intruders in restricted areas. Suggest an API suitable for this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with EfficientNet-B0 for image classification from PyTorch Hub. This high-performance model can be fine-tuned to detect intruders in restricted areas.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"An autonomous vehicle company requires an API capable of object detection, drivable area segmentation, and lane detection. Provide the API and an example code to fulfill their requirements.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub, which can perform object detection, drivable area segmentation, and lane detection tasks.', '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)\"}###"}
{"prompt":"I want to calculate the relative depth of a scene from a single photo. Recommend an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Depth Estimation', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which is designed to compute relative inverse depth from a single image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)'}###"}
{"prompt":"I have a photo of an outdoor landscape, and I want to determine the relative depth of objects in the image. Provide an API that will help me achieve this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub to compute the relative depth of objects in a single image.', 'code': \"import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\"}###"}
{"prompt":"I want an API to separate music into vocals, drums, bass, and other instruments for a music application I am developing. What API should I use?\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"model = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the Open-Unmix model from PyTorch Hub for separating music into vocals, drums, bass, and other instruments. The model is pretrained on the MUSDB18 dataset.', 'code': 'import torch\nseparator = torch.hub.load(\"sigsep\/open-unmix-pytorch\", \"umxhq\", pretrained=True)'}###"}
{"prompt":"Make a list of semantic audio conversion tasks, and specify a suitable API for speech to text conversion from various audio input formats.\n\n###\n\n","completion":" {'list_of_tasks': ['Speech-to-text conversion', 'Voice recognition', 'Language translation', 'Audio classification', 'Transcription services'], 'recommended_api': {'domain': 'Speech-To-Text', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_stt', language='en', device=device)\", 'api_provider': 'PyTorch', 'explanation': 'Use Silero Models from PyTorch Hub for speech-to-text conversion, supporting various audio input formats, languages, and dialects.', 'code': 'import torch\ndevice = torch.device('cpu')\nmodel, decoder, utils = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='en', device=device)'}###"}
{"prompt":"I am building a bird identification app but I have trouble indentifying birds from photo. Which API can I use to indentify birds given a photo?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NTS-Net model from PyTorch Hub to identify bird species in a given photo. This model is specifically trained on the CUB200 2011 dataset of bird species.', 'code': 'import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{\"topN\": 6, \"device\":\"cpu\", \"num_classes\": 200})'}###"}
{"prompt":"A business meeting is being transcribed, and we need to detect speech segments in the recorded audio. Provide me with an API to perform voice activity detection on the audio file.\n\n###\n\n","completion":" {'domain': 'Voice Activity Detection', 'api_call': 'model = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Voice Activity Detector from PyTorch Hub. The model can be used to detect speech segments within an audio file for efficient transcription.', 'code': 'import torch\nmodel, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)'}###"}
{"prompt":"Identify an API to perform semantic segmentation of the street labeled with road, sidewalk, and building for a given picture.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet-50 backbone from PyTorch Hub, which can be fine-tuned to perform semantic segmentation of street elements like roads, sidewalks, and buildings.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"A photographer wants to separate the foreground and background in their images using semantic segmentation. Suggest an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model for semantic segmentation from PyTorch Hub to separate the foreground and background in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)'}###"}
{"prompt":"Propose a neural network model to use for detecting furniture in photos and identifying their types.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5 model from PyTorch Hub, which can be fine-tuned for detecting furniture in images and classifying them into types.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"Alex is a deaf user who needs an app to convert text to voice. Which API would you recommend for that?\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_tts', language='en', speaker='lj_16khz')\", 'api_provider': 'PyTorch', 'explanation': 'Use the Silero Text-To-Speech Models to convert text to voice with a single line of code. They provide high-quality voice synthesis for various languages.', '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)\"}###"}
{"prompt":"Recommend me an API that identifies animals in a given image to feed as data to other functionalities.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext-101 32x16d WSL model from PyTorch Hub, which can be fine-tuned for animal classification given an image.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"We are building an image classifier for our meme sharing platform. Suggest an API for high-accuracy image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub, which can achieve state-of-the-art accuracy for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)'}###"}
{"prompt":"Suggest an API that can identify the species of a bird from an image taken in Yosemite National Park.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt WSL model from PyTorch Hub, which can be fine-tuned for bird species classification using an image taken in Yosemite National Park.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"An artist is trying to generate new art from existing 64x64 art images for an online gallery. Provide me an API that can generate new images utilizing training data of previously generated art images.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DCGAN model from PyTorch Hub, which can be fine-tuned with existing 64x64 art images to generate new images for the online gallery.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"I want to know more about ResNet50 from NVIDIA Deep Learning Examples. Provide me with an API that I can use in my system to classify images with high precision.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from NVIDIA Deep Learning Examples using PyTorch Hub for high-precision image classification tasks.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\"}###"}
{"prompt":"I need to create an image of a realistic building. Is there an API that can help me with this?\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'The pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub can generate high-quality, realistic images. However, this model is specifically trained on human faces. You may need to find or train a similar model on a dataset containing images of buildings.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"A medical researcher from Johns Hopkins University wants to analyze brain MRI scans for tumor detection. Recommend an API that can perform tumor segmentation in brain MRI images.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained U-Net model for brain MRI segmentation from PyTorch Hub, which can generate segmentation maps for detecting tumors in brain MRI images.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"Can you help me find an API for image classification which can recognize the objects in a given image?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_resneXt\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNeXt101-32x4d model from PyTorch Hub for image classification, which can recognize the objects in a given image.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_resneXt\", pretrained=True)'}###"}
{"prompt":"Derive a recommendation for a CNN used in image classification, trained on the ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101-32x4d model from PyTorch Hub for image classification. This model is trained on the ImageNet dataset and augmented with Squeeze-and-Excitation modules.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"What is an API that does an automatic image classification of flowers?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-121 model from PyTorch Hub, which can be fine-tuned for automatic flower classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"A company wants to develop a text classification model for their customer support operations. Suggest an API that uses transfer learning to achieve this.\n\n###\n\n","completion":" {'domain': 'Text Classification', 'api_call': \"model = torch.hub.load(repo_or_dir='pytorch\/fairseq', model='roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained RoBERTa model for text classification from PyTorch Hub, which is a robustly optimized version of BERT, capable of efficiently classifying texts using transfer learning.\", 'code': 'import torch\nmodel = torch.hub.load(repo_or_dir=\"pytorch\/fairseq\", model=\"roberta.large\", pretrained=True)'}###"}
{"prompt":"Suggest an API able to classify images with a high level of accuracy, using the fewest possible parameters.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-201 model from PyTorch Hub, which efficiently classifies images with high accuracy and fewer parameters.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"I need a model for classifying images, and I want to use Densenet-169. Give me an API to use.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained DenseNet-169 model from PyTorch Hub for image classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"Design an image classification model to recognize different objects in images. Recommend an API appropriate for this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-161 model from PyTorch Hub for image classification tasks, which can recognize various objects and categories in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)'}###"}
{"prompt":"Find a suitable API to perform semantic segmentation of an aerial image into building, road, and vegetation.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet101 model from PyTorch Hub to segment aerial images into building, road, and vegetation. You will need to fine-tune the model on an appropriate dataset for this task.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)'}###"}
{"prompt":"Recommend an API that can classify images using a fast and efficient model.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet (Harmonic DenseNet) model from PyTorch Hub to quickly and efficiently classify images.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"Can you suggest an efficient API for classifying a large collection of images that minimizes computational cost?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GhostNet model from PyTorch Hub, which is designed for efficient image classification with minimal computational cost.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)'}###"}
{"prompt":"Identify an API that can classify flora and fauna species by analyzing images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HarDNet model from PyTorch Hub, which can be fine-tuned for classifying flora and fauna species based on input images.\", 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"A robotics company wants to reduce the computational and memory cost of an image classification model for mobile robot applications. Suggest an API suited for this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68DS model from PyTorch Hub, as it is designed for mobile applications and offers low computational and memory cost.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)'}###"}
{"prompt":"I need an API to help identify dog breeds in images. Recommend one with a light model that is fast in processing the images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68 model from PyTorch Hub, known for its fast and efficient image classification. You can fine-tune it to identify dog breeds from images.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"Find an API suitable for cross-domain image classification with high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt-101-IBN-a model from PyTorch Hub, which is suitable for cross-domain image classification tasks and has high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"Recommend an API that can create models having domain or appearance invariance.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net (ResNet-101-IBN-a) model from PyTorch Hub for creating models with domain or appearance invariance.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"I need to re-identify a person in a different set of images, suggest an API I can use to make this process easier.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a model from PyTorch Hub, which is designed for tasks like person re-identification, due to its domain\/appearance invariance.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"Find me an API for classifying different breeds of dogs from their photos.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Inception v3 model from PyTorch Hub, which can be fine-tuned for dog breed classification from images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)'}###"}
{"prompt":"My manager just asked me to implement a classification system that can categorize pictures into different animal species. Can you recommend an API that can help me with this task?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNeSt model from PyTorch Hub, which can be fine-tuned for the classification task of categorizing pictures into different animal species.\", 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"Detect the type of tree species in a park from an image taken by a smartphone. Provide an API that can help with this classification problem.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-101 model from PyTorch Hub, which can be fine-tuned for tree species classification from smartphone images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"Many people loves to create different memes. Propose an API that can use keywords from meme text to detect if a meme is negative or not.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model from PyTorch Hub, which can be fine-tuned for meme sentiment classification based on text keywords.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)'}###"}
{"prompt":"Can you find an API that can identify objects in images?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-269 model from PyTorch Hub for image classification, which can identify objects within images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Tell me an API that can classify a given image into one of the predefined categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load a pre-trained ResNet18 model from PyTorch Hub for image classification, which can classify an image into one of the pre-defined categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)'}###"}
{"prompt":"I want to perform classification on my dataset with a model that is optimized for a CPU. Suggest me an API.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the ProxylessNAS model optimized for CPU from PyTorch Hub for efficient classification on your dataset.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)'}###"}
{"prompt":"I want to classify images on my mobile efficiently. Suggest an API to achieve this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS Mobile model from PyTorch Hub, which is optimized for high-efficiency image classification on mobile devices.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"A company needs to classify a large number of photos by their content. Suggest an API that is optimized for GPU and can perform classifications efficiently.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS GPU-optimized model from PyTorch Hub, which can efficiently classify images using a GPU.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"I need to create an app that can analyze food image and can tell its catagory like pizza, burger, etc. Write an API that can help me in achieving this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model from PyTorch Hub, which can be fine-tuned for food classification given an input image of food items like pizza, burger, etc.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"Design me an API for classifying images using a pretrained model.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained ResNet34 model from PyTorch Hub for image classification.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"Can you customize an API that can detect eco-friendly packaging from photos?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet101 model from PyTorch Hub, which can be fine-tuned for eco-friendly packaging detection from photos.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)'}###"}
{"prompt":"Identify an API that can classify types of dogs from a given image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet152 model from PyTorch Hub, which can be fine-tuned for dog breed classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"Can you suggest an API that will help a company build an image classification model for their photo sharing app to identify objects in the photos?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext101 model from PyTorch Hub for image classification to identify objects in the photos.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnext101_32x4d\", pretrained=True)'}###"}
{"prompt":"I'm working on a computer vision project that requires a deep learning model for image classification. Can you provide an API that is efficient and accurate for this task?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ResNext model from PyTorch Hub which is efficient and accurate for image classification tasks. Specifically, the \"resnext50_32x4d\" model is pretrained on ImageNet.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\"}###"}
{"prompt":"Find me an API to classify clothing items from a real-time camera feed.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Tiny model from PyTorch Hub. It can be fine-tuned for clothing item classification from a real-time camera feed.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)'}###"}
{"prompt":"I want to classify everyday objects in images. Is there an AI model that can help me to do this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP model from PyTorch Hub to classify everyday objects in images with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"Help me find an image classifier model that can be used in a mobile application to classify everyday objects from images. The model should be light and efficient.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load(\"huawei-noah\/Efficient-AI-Backbones\", \"snnmlp_b\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Base model for efficient image classification from PyTorch Hub for recognizing everyday objects.', 'code': 'import torch\nmodel = torch.hub.load(\"huawei-noah\/Efficient-AI-Backbones\", \"snnmlp_b\", pretrained=True)'}###"}
{"prompt":"Can you suggest an API that can classify objects in images with a high level of accuracy but maintaining small model size and low computational cost?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the SqueezeNet model from PyTorch Hub, which provides high image classification accuracy with reduced model size and low computational cost.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)'}###"}
{"prompt":"Is there any image classification API that I can use for tiny object, e.g., fruits in the market images, to save memory on my smartphone application?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet 1.1 model from PyTorch Hub, which consumes less memory and provides good accuracy suitable for small objects such as fruits in market images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)'}###"}
{"prompt":"Help me recognize the objects present in an image. Recommend an API that can do this.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model with batch normalization from PyTorch Hub to recognize objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"I need a way to efficiently detect and categorize various types of plants and animals in images taken from a nature reserve, could you provide an API that could help me with this task using a pretrained model?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'torch.hub.load(repo_or_dir='pytorch\/vision', model='vgg11', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from PyTorch Hub, which can be fine-tuned for plant and animal classification in nature reserve images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"I'm looking for an API that recognizes different objects in images. Can you recommend one?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG13 model from PyTorch Hub for image recognition tasks, as it's capable of recognizing different objects in images.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)'}###"}
{"prompt":"Help me classify species of birds from images using an API call.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub, which can be fine-tuned for bird species classification from images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"Help me identify the objects in a photo using an API that is pretrained with many different classes.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model with batch normalization from the PyTorch Hub to recognize objects in a given photo.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)'}###"}
{"prompt":"Our application identifies objects in images. What is the best image recognition API from torchvision for our case?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model from torchvision for image recognition tasks. VGG19 is a popular and effective model for large-scale image recognition.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)'}###"}
{"prompt":"Recommend an API that can recognize animals in an image and classify them into categories like pets, wildlife, and aquatic animals.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('kadhiravanarasu\/project-zoo', 'model', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load a custom-trained model for recognizing and classifying animals in images into pets, wildlife, and aquatic animals.', 'code': 'import torch\nmodel = torch.hub.load('kadhiravanarasu\/project-zoo', 'model', pretrained=True)'}###"}
{"prompt":"What is an API that can classify photos of foods into their meal category?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet50_2 model from PyTorch Hub, which can be fine-tuned for food image classification into meal categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)'}###"}
{"prompt":"Find me a powerful API that can classify dozens of classes from an image taken.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-101 model from PyTorch Hub, a powerful image classification model that can classify numerous classes.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"A sport streaming platform needs to detect clips of football plays for highlight creation. Recommend an API that can classify video clips into different action categories.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': 'Load the pretrained SlowFast Networks model from PyTorchVideo Hub, which can classify video clips into various action categories, including football plays.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)'}###"}
{"prompt":"I am developing a video platform and I need an API that can classify the type of sports played in the video.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained X3D model for video classification from PyTorch Hub to classify video clips into different action categories, including various sports.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"Recommend a machine learning API for classifying images with high inference speed and low latency on NVIDIA GPUs.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GPUNet model from NVIDIA Deep Learning Examples. It is optimized for NVIDIA GPUs and offers high inference speed and low latency.', 'code': \"import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\"}###"}
{"prompt":"Recommend an image classification API that can be used for classifying medical instruments in a biomedical dataset.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-18 model with semi-supervised and semi-weakly supervised ImageNet from PyTorch Hub, which can be fine-tuned for biomedical instrument classification.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"There is a need at an NGO to develop a model that can analyze and classify the comments of the articles they publish. Tell me an API that can do this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\", 'api_provider': 'PyTorch', 'explanation': 'Load the BERT model from the Hugging Face PyTorch-Transformers library. This model is capable of analyzing and classifying text, such as comments.', 'code': 'import torch\nmodel = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased)'}###"}
{"prompt":"A freelance translator wants to automate their translation process from English to French. Can you suggest an API that can perform neural machine translation from English to French?\n\n###\n\n","completion":" {'domain': 'Text Translation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Transformer model for neural machine translation from PyTorch Hub, which is specifically trained for translating English to French text.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\"}###"}
{"prompt":"CrateChef is a food subscription service and wants to develop a plate classifier for their users. Suggest an API to classify images of food plates.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet-B0 model for image classification from PyTorch Hub to classify images of food plates.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"In a smart city project, I need to find an API that can detect multiple objects such as cars or stop signs from an image provided by surveillance cameras.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the Single Shot MultiBox Detector (SSD) model from PyTorch Hub, which is capable of detecting multiple objects such as cars and stop signs in images from surveillance cameras.\", 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"Develop a text-to-speech solution to help visually impaired users read material. Propose an API that can do this.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Tacotron 2 model from PyTorch Hub to generate natural-sounding speech from text input, suitable for helping visually impaired users.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')'}###"}
{"prompt":"A developer needs an API for image classification that can classify images without image augmentation and has a higher top-1 accuracy, suggest an API for the same.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V1', 'mealv1_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the MEAL_V2 model for image classification from PyTorch Hub, which is designed to have high top-1 accuracy without the need for image augmentation or other tricks.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V1', 'mealv1_resnest50', pretrained=True)\"}###"}
{"prompt":"As a developer at an app company, I need an API to classify images from users' phone galleries. Please suggest a suitable API.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MEAL V2 model, with a pretrained ResNet50 backbone, for image classification. It achieves 80.98% top-1 accuracy on ImageNet without using any specialized tricks.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I work in a tech company and we want to identify different objects from images. Give me an API that can perform image classification tasks.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MEAL V2 ResNet50 model from PyTorch Hub with pretrained weights for image classification, which can identify various objects from images.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"Recommend an API to classify large datasets of photographs into categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub for image classification tasks on large datasets. This model achieves high accuracy without requiring any tricks or architecture modifications.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\"}###"}
{"prompt":"I am experimenting with models to train them on images of newspaper covers to classify them according to their content. Do you know any API that would be highly accurate and will work on this task?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MEAL V2 model with MobileNetV3 architecture from PyTorch Hub, which is highly accurate for image classification tasks and can be fine-tuned for classifying newspaper covers based on their content.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"Suggest an API that can classify images into a variety of categories, such as animals or objects, given an image as input.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 ResNet-50 model from PyTorch Hub, which is capable of classifying input images into a wide range of categories, such as animals and objects.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\"}###"}
{"prompt":"Nike is building an automated system to classify shoes in their inventory. Suggest an API to classify images for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL-V2 model with EfficientNet-B0 architecture from PyTorch Hub to classify images of shoes into different categories.', 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)'}###"}
{"prompt":"A zookeeper wants to create an app that can classify the animal types in a picture. Suggest an image classification API that could be implemented.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(\"szq0214\/MEAL-V2\", \"meal_v2\", \"mealv2_mobilenet_v3_large_100\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 model with MobileNet V3-Large architecture from PyTorch Hub, which can be used for animal classification in images.', 'code': 'import torch\nmodel = torch.hub.load(\"szq0214\/MEAL-V2\", \"meal_v2\", \"mealv2_mobilenet_v3_large_100\", pretrained=True)'}###"}
{"prompt":"I need an API to categorize sports activities from a given video clip.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained 3D ResNet model (slow_r50) from PyTorch Hub, which can be fine-tuned for sports activities classification from video clips.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"My self-driving car company requires an API for perception tasks like detecting traffic objects, drivable area segmentation, and lane detection. Can you provide one?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HybridNets model from PyTorch Hub to perform multiple perception tasks, including traffic object detection, drivable area segmentation, and lane detection for self-driving cars.', 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"What would be an API to estimate the relative depth of objects in a provided image?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained MiDaS DPT_Hybrid model from PyTorch Hub to compute relative inverse depth from a single input image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)'}###"}
{"prompt":"Suggest an API to estimate the depth from a single image for an obstacle detection system in a drone surveillance application.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which can estimate relative depth from a single image, suitable for an obstacle detection system in drone surveillance applications.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"Recommend an API that can perform real-time object detection, drivable area segmentation, and lane detection for an autonomous vehicle.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub, which can perform real-time object detection, drivable area segmentation, and lane detection for autonomous vehicles.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"I work at Spotify and I'm looking for an API that can separate the vocals, drums, bass, and other instruments from a pop music track.\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"model = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Open-Unmix model from PyTorch Hub to separate pop music into vocals, drums, bass, and other instruments.', 'code': 'import torch\nseparator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)'}###"}
{"prompt":"Identify a suitable API for bird species recognition in images for a wildlife photographer.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\", 'api_provider': 'PyTorch', 'explanation': 'Load the NTS-Net model from PyTorch Hub, which is pre-trained on the CUB200-2011 dataset for fine-grained bird species recognition.', 'code': \"import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\"}###"}
{"prompt":"Provide an API that can segment objects present in a given image.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 with ResNet-101 backbone model from PyTorch Hub for semantic segmentation, which segments objects present in an input image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)'}###"}
{"prompt":"Help me in segmenting objects in an image. Suggest an API that I can use for this purpose.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet-50 backbone from PyTorch Hub for semantic segmentation of objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"A podcast producer needs a solution to detect speech in audio recordings. Recommend an API to recognize the timestamps where voice is present in an audio file.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Voice Activity Detector (VAD) from PyTorch Hub to detect speech and output timestamps where voice is present in an audio file.', 'code': \"import torch\nmodel, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\"}###"}
{"prompt":"Suggest an API that can transcribe spoken words in a German audio file to written text.\n\n###\n\n","completion":" {'domain': 'Speech-To-Text', 'api_call': \"model, decoder, utils = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='de', device=device)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Speech-To-Text model with German language support from PyTorch Hub to transcribe spoken words in an audio file to written text.', 'code': 'import torch\ndevice = torch.device(\"cpu\")\nmodel, decoder, utils = torch.hub.load(repo_or_dir=\"snakers4\/silero-models\", model=\"silero_stt\", language=\"de\", device=device)'}###"}
{"prompt":"I want to count the number of people in a crowd using an image. Provide me with an API that can do this.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': 'model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5s model from PyTorch Hub, which can detect and localize people in an image.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"I want to create some new clothing designs using AI. Which API should I use to generate diverse clothing designs?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DCGAN model from PyTorch Hub, which can generate diverse clothing designs using generative adversarial networks.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"I want to implement an image classification model for an app, can you suggest an API that I can use and give me the code snippet to load the model?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model for image classification from PyTorch Hub to classify images with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl')\nmodel.eval()'}###"}
{"prompt":"Tell me an API that can accurately classify a wide range of images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub for accurate image classification across a wide range of categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)'}###"}
{"prompt":"A researcher needs to categorize real world objects in images using machine learning. Provide a suitable API for image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub for image classification. This model achieves state-of-the-art accuracy and works well for categorizing objects in images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"I am developing a visual content recognition system for an e-commerce website to categorize images. Recommend an API that can help me with image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained ResNext WSL model from PyTorch Hub for image classification. This model provides state-of-the-art performance and is suitable for e-commerce image categorization.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Discover an API suitable for segmenting abnormal regions in brain MRI scans.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained U-Net model for biomedical image segmentation, specifically designed for abnormality segmentation in brain MRI scans.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"I need an API that I can use to create a project for a Tech event about classifying several bird species for local naturalists.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet50 v1.5 model from PyTorch Hub and fine-tune it for bird species classification tasks to suit local naturalists' needs.\", 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)'}###"}
{"prompt":"Can you provide a GAN architecture that can generate high-resolution images progressively?\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub. This method generates high-resolution images by progressively increasing the resolution throughout the training process, resulting in improved quality, stability, and variation.\", 'code': \"import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\"}###"}
{"prompt":"\"I'm building an app for tourists to identify famous landmarks based on their photos. Suggest an API.\"\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained AlexNet model from PyTorch Hub and fine-tune it for landmark recognition to efficiently identify famous landmarks in tourists' photos.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\"}###"}
{"prompt":"Tell me an API that can efficiently classify thousands of real-world objects from an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use the SE-ResNeXt101-32x4d model from PyTorch Hub. It is a pre-trained model, designed to identify thousands of real-world objects within an image efficiently.\", 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_se_resnext101_32x4d\", pretrained=True)'}###"}
{"prompt":"I have the URL of an image and I want to classify it in real-time. Provide me an API that can classify the image and output 5 top predictions.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt101-32x4d model provided by NVIDIA and DeepLearningExamples from PyTorch Hub. This model can classify the input image and return the top 5 predictions.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_resneXt\", pretrained=True)'}###"}
{"prompt":"Recommend an API capable of converting given text to natural-sounding speech.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained WaveGlow model from NVIDIA for speech synthesis. Combine Tacotron 2 and WaveGlow models to create a text-to-speech system that synthesizes natural-sounding speech from raw text.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\"}###"}
{"prompt":"I want to train a sentiment analysis model to classify Yelp reviews. Suggest an API for a good base model that I can fine-tune.\n\n###\n\n","completion":" {'domain': 'Text Classification', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained RoBERTa-large model from PyTorch Hub, which can be fine-tuned for sentiment analysis on Yelp reviews.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)'}###"}
{"prompt":"I am developing an image classification application. What API can I use to perform classification using a Dense Convolutional Network?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-121 model from PyTorch Hub for image classification using a Dense Convolutional Network.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"Please recommend an API suitable for image classification that can identify objects contained in arbitrary photographs.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Densenet-201 model from PyTorch Hub with pre-trained weights for image classification, capable of identifying objects in various images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"A zoo automated their entrance control system, and they need a program that recognizes animal types to monitor statistics. Please provide me with an API to classify animals based on their images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-169 model from PyTorch Hub, which can be fine-tuned for animal classification given images of different animals.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"I am a software developer working on an autonomous car project. I need an API to segment an image so I can identify different objects in the area.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the FCN-ResNet101 model with pretrained weights from the PyTorch hub to semantically segment input images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"fcn_resnet101\", pretrained=True)'}###"}
{"prompt":"I have a project where I am asked to perform semantic segmentation on cityscape images. Recommend me an API that would be useful for this.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the Fully-Convolutional Network (FCN-ResNet50) API from the PyTorch Vision library, pretrained for semantic segmentation.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\"}###"}
{"prompt":"I am a software engineer developing a mobile application capable of classifying images. Please provide me with an API for image classification in PyTorch that has low memory requirements and high efficiency.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-39DS model from PyTorch Hub, which is fast, efficient, and designed for low memory traffic CNN applications.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"Recommend an API that can classify objects in images efficiently, suitable to run on smartphones.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68DS model from PyTorch Hub, which is a low memory traffic CNN optimized for smartphones and other resource-limited devices.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)'}###"}
{"prompt":"I work at Google Photos and my team needs an API that can efficiently classify photos into different categories. What do you suggest?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-85 model for image classification from PyTorch Hub to efficiently classify photos into different categories.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"Identify an API that can classify objects in images quickly and requires low memory.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68 model from PyTorch Hub, which is fast, efficient, and has low memory requirements, for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"A teenager wants their child to play with his new toy cars, but they want the phone to recognize the cars using machine vision. Give me an API that is computationally cheap and could recognize toy cars.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the GhostNet model from PyTorch Hub, which is designed for efficiency and can be fine-tuned to recognize toy cars using machine vision.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)'}###"}
{"prompt":"Invent a smart camera system for a library that is capable of finding the book section from an image. Recommend an API capable of doing this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GoogLeNet model from PyTorch Hub, which can be fine-tuned for classifying book sections in a library based on images.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\"}###"}
{"prompt":"Our team needs an image classification model for cross-domain tasks. Recommend a pre-trained model we can use in a project.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-101-IBN-a model from PyTorch Hub that has domain\/appearance invariance, making it suitable for cross-domain tasks.', 'code': 'import torch\nmodel = torch.hub.load(\"XingangPan\/IBN-Net\", \"resnet101_ibn_a\", pretrained=True)'}###"}
{"prompt":"A fashion E-commerce platform needs to predict clothing types from images. Suggest me a machine learning API that can be used to classify clothing items.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a (IBN-Net) model from PyTorch Hub. This high-performance model can be fine-tuned for classifying clothing types from images provided by a fashion e-commerce platform.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\nmodel.eval()' }###"}
{"prompt":"I need a model that can classify various objects in images while being capable of distinguishing between a wide range of domains and appearances. Can you suggest an API for this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the IBN-Net model from PyTorch Hub, which unifies instance normalization and batch normalization for cross-domain object classification.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"Suggest a suitable API for cross-domain classification tasks like person re-identification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net (ResNeXt-101-IBN-a) model from PyTorch Hub, which is specifically designed for cross-domain classification tasks and provides domain\/appearance invariance.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"Can you give me an API capable of identifying animal species from a provided image?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt50 model from PyTorch Hub, which can be fine-tuned for animal species identification from an image.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"I have images of various objects and I need to classify them into different categories. Can you suggest an API that can help me with this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNeSt model for image classification from PyTorch Hub to classify images into different categories.\", 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)'}###"}
{"prompt":"Create an instance of an API that takes an image of a bird and predicts what type of bird it is.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-101 model from PyTorch Hub, which may be fine-tuned for bird species identification by training on a dataset of bird images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"Find me an efficient API for image classification that's optimized for better performance on CPUs.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ProxylessNAS model from PyTorch Hub, which is specifically optimized for better performance on CPUs while maintaining high accuracy for image classification tasks.\", 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)'}###"}
{"prompt":"I need to classify objects using an efficient model optimized for GPU. Provide me an API for that purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ProxylessNAS model from PyTorch Hub, which is optimized for GPU and offers efficient performance for object classification tasks.\", 'code': \"import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\"}###"}
{"prompt":"We need to develop an object detection app for mobile devices. Suggest an API for a neural network based on ProxylessNAS that is optimized for mobile devices.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS Mobile model from PyTorch Hub, which is specialized for object detection on mobile devices.', 'code': \"import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\"}###"}
{"prompt":"I want to create an app that identifies dog breeds from user uploaded images. Suggest an API that could recognize different dog breeds from images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model from PyTorch Hub, which can be fine-tuned for dog breed recognition in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"I have a dataset of images and want to classify them into different categories. Suggest an API that can do this task with high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from PyTorch Hub, which offers high accuracy for classifying images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I am working on a project that requires me to identify objects in an image. Find me an API that can do the task with pretrained models.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained ResNet34 model from PyTorch Hub for object identification in images.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Can you assist me in selecting an API that automatically sorts through images of different kinds of animals?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet152 model from PyTorch Hub, which can be used for sorting images of different animals.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"Develop a program to categorize animals' species from a set of images with the help of an API.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext50_32x4d model from PyTorch Hub and fine-tune it for animal species classification. The model can then be used to categorize animals from a set of images.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\"}###"}
{"prompt":"A museum wants to build an image classifier for paintings. Recommend me an appropriate API that can be fine-tuned for painting classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(repo_or_dir=\"pytorch\/vision\", model=\"resnext101_32x4d\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext model from PyTorch Hub, which can be fine-tuned for painting classification.', 'code': \"import torch\nfrom torchvision import models\nmodel = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"A startup is in need of an efficient image classification API. Tell me about an API that can classify images effectively.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP Small model from PyTorch Hub for efficient and accurate image classification.', 'code': 'import torch\nmodel = torch.hub.load(\"huawei-noah\/Efficient-AI-Backbones\", \"snnmlp_s\", pretrained=True)'}###"}
{"prompt":"Find me an API that can recognize dog breeds in a given image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SqueezeNet model from PyTorch Hub, which can be fine-tuned to identify dog breeds in a given image.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\"}###"}
{"prompt":"What are the operational concepts for replacing an old neural network mechanism facilitating an MLP model with better accuracy?\n\n###\n\n","completion":" {'domain': 'Neural Network Improvement', 'solution': {'mechanism': 'Leaky Integrate-and-Fire (LIF) neurons', 'api_call': \"torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'principle': 'SNNMLP utilizes the LIF mechanism to enhance the MLP model accuracy without additional FLOPs'}, 'explanation': 'LIF neurons are implemented in the SNNMLP model through full-precision LIF operations for communication between patches, horizontal and vertical LIF neurons in different directions, and group LIF for better local feature extraction. The resulting model achieves significantly higher top-1 accuracy on the ImageNet dataset with the same FLOPs.'}###"}
{"prompt":"We are looking to classify the make and model of a car from visible images. Can you provide an API to accomplish this?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP model from PyTorch Hub, which can be fine-tuned to classify car make and model from images.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)'}###"}
{"prompt":"Design an image classification system for a smart doorbell that aims to classify different types of animals like cats, dogs, and birds. Propose a machine learning API suitable for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the SqueezeNet model from PyTorch Hub, which is an image classification model suitable for classifying animals like cats, dogs, and birds.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)'}###"}
{"prompt":"A startup is building a robot to classify street objects. Tell me an API that can detect objects in a street view photograph and provide efficient inference.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ShuffleNet v2 model from PyTorch Hub, which is optimized for speed and memory, making it suitable for detecting objects in street view images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)'}###"}
{"prompt":"Can you suggest an API I can use to classify various types of plants from their images?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model from PyTorch Hub, which can be fine-tuned for plant classification from images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"vgg13\", pretrained=True)'}###"}
{"prompt":"Help me to identify what objects are in an image. Can you suggest an API that is capable of doing this?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from PyTorch Hub, which is capable of recognizing objects within an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"What is a pre-trained model I can use to recognize objects in a picture?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model with batch normalization from PyTorch Hub for image recognition and object detection.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)'}###"}
{"prompt":"I am building a software for an AI art gallery, and it needs to recognize artistic characteristics of different famous painting images. Identify a pre-trained model which can help me in this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load a pretrained VGG11 with batch normalization model from the PyTorch Hub which can be fine-tuned for recognizing artistic characteristics in famous paintings.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"I have a bunch of pictures of plants, and I need an API that can identify the species for me.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub, which can be fine-tuned to recognize and classify plant species accurately.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"An app developer needs a machine learning API for recognizing images taken by users to suggest appropriate captions for those images, recommend an API for that.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG-19 model from PyTorch Hub, which can be used for image recognition to suggest suitable captions based on the recognized objects in the user's images.\", 'code': 'import torch\nmodel = torch.hub.load(\\'pytorch\/vision\\', \\'vgg19\\', pretrained=True)'}###"}
{"prompt":"Recommend an API that will help me classify animals and birds in high quality images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet101_2 model from PyTorch Hub for classifying animals and birds in high-quality images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"I need an image recognition API. Can you please suggest a model?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub for image recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"I am a content moderator for a video streaming platform, and I need a pretrained API to identify category of actions performed in a clip. What do you recommend?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': \"Load the pretrained X3D-Small model from Facebook's PyTorchVideo for efficient video clip classification into various action categories.\", 'code': 'import torch\nmodel = torch.hub.load(\"facebookresearch\/pytorchvideo\", \"x3d_s\", pretrained=True)'}###"}
{"prompt":"I built an AI personal trainer app that uses images from users' workouts to give live feedback. Recommend a model for classifying these workout images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load a pretrained Wide ResNet-50-2 model from PyTorch Hub, which is robust and suitable for classifying images of users\\' workouts.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)'}###"}
{"prompt":"Tell me an API that can be used to classify different actions happening in a video clip.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': \"Load the pretrained SlowFast Networks model from PyTorchVideo Hub, which is capable of classifying video clips into different action categories based on the Kinetics 400 dataset.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)'}###"}
{"prompt":"Recommend an image classification API capable of working efficiently on NVIDIA GPUs.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GPUNet model from NVIDIA Deep Learning Examples library in PyTorch Hub. GPUNet is optimized for NVIDIA GPU and TensorRT performance, offering efficient image classification.', 'code': \"import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\ngpunet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\"}###"}
{"prompt":"We need to compare the performance of an NLP model across different frameworks, can you provide an API to load a pretrained transformer model for sequence classification?\n\n###\n\n","completion":" {'domain': 'Natural Language Processing', 'api_call': \"model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained BERT model from PyTorch-Transformers library, which can be used for sequence classification tasks.', 'code': \"import torch\nmodel = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\"}###"}
{"prompt":"I want to quickly and efficiently classify images, I need an API for Once-for-all Networks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load(repo_or_dir='mit-han-lab\/once-for-all', model='ofa_supernet_mbv3_w10', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Once-for-all (OFA) Networks model from PyTorch Hub to efficiently classify images across different devices with varying resource constraints.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"A startup company is working on a new app where customers can upload photos and have them automatically categorized into objects. Suggest an API that can recognize objects in images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained ResNet-18 model from Facebook Research, which is trained on semi-supervised and semi-weakly supervised ImageNet datasets for object recognition in images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"I am building a mobile app that classifies pictures of cats and dogs. Recommend an API for image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet-B0 model for image classification from PyTorch Hub to classify images of cats and dogs with high efficiency and low computational cost.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"Which API can I use to build a powerful English to French translation system?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': 'Use the Transformer (NMT) model from PyTorch Hub for building an English to French translation system based on state-of-the-art sequence-to-sequence modeling.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/fairseq\", \"transformer.wmt14.en-fr\", tokenizer=\"moses\", bpe=\"subword_nmt\")'}###"}
{"prompt":"Tell me a model to use if I want to identify different objects in images without having my own dataset.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Single Shot MultiBox Detector (SSD) model from PyTorch Hub, which is trained on the COCO dataset and can detect objects in images accurately and efficiently.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)'}###"}
{"prompt":"Let me know about an API that can convert English text into speech.\n\n###\n\n","completion":" {'domain': 'Speech Synthesis', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Load NVIDIA Tacotron 2 from PyTorch Hub. It is a neural network model that generates natural-sounding speech from English text.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\"}###"}
{"prompt":"I want to build an Instagram-like app that can tag dogs based on their breed. What API can help me with this?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 model with ResNet-50 from PyTorch Hub, which can be fine-tuned for dog breed classification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"Propose a way to classify random images with an efficiency of around 80% without any tricks.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL-V2 model based on ensemble knowledge distillation from PyTorch Hub. MEAL-V2 models achieve state-of-the-art results (~80% Top-1 accuracy on ImageNet) without relying on common tricks and enhancements.', 'code': \"import torch\\n\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I need an API to help me identify different breeds of dogs in images. Can you find me one?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model from PyTorch Hub, which can be fine-tuned for dog breed identification in images.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)\"}###"}
{"prompt":"I am developing an app that can recognize different dog breeds from a photo. Can you recommend an API for this task?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3-Small 0.75 architecture from PyTorch Hub for image classification. This model can be fine-tuned for dog breed identification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"I have an image of an object, and I need an API to classify it. Ensure the API provides a high accuracy without tricks like architecture modification or mixup\/cutmix training.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model from PyTorch Hub for image classification. This model achieves high accuracy without tricks like architecture modification, mixup\/cutmix training, or additional outside training data.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I need an API that can identify the type of fruit in an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL-V2 model from PyTorch Hub, which can be fine-tuned for fruit classification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_100', pretrained=True)\"}###"}
{"prompt":"We want to build an application that recognize objects in an image. What API can classify these images for us?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model with MobileNet V3-Large architecture from PyTorch Hub to classify objects in images.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"Recommend an API to analyze workout videos and classify them into different exercise categories.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained 3D ResNet (slow_r50) model from PyTorch Hub to classify workout videos into different exercise categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"Identify an API that can detect vehicles, drivable areas, and lanes from images of roads.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load(repo_or_dir='datvuthanh\/hybridnets', model='hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HybridNets model from PyTorch Hub, which can detect vehicles, drivable areas, and lanes from road images.', 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"Describe an API suitable for recognizing objects in an image with at least 80% accuracy on the ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model with EfficientNet-B0 from PyTorch Hub, which achieves 80%+ Top-1 accuracy on the ImageNet dataset for object recognition tasks.\", 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)'}###"}
{"prompt":"Find me an API that can compute depth information from a single image of a scene.\n\n###\n\n","completion":" {'domain': 'Image Depth Estimation', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub to compute depth information from a single image.', 'code': \"import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\"}###"}
{"prompt":"A friend is building a robot that can detect distance of object from camera. Provide an ML API that computes relative depth using a single image input.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model for computing relative depth from a single image in PyTorch Hub. It can be used to estimate object distances given a camera input.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)'}###"}
{"prompt":"Tell me about an API that can estimate depth for single images, suitable for real-time scenarios like autonomous vehicles.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MiDaS_small model from PyTorch Hub, which can compute relative depth from a single image and is suitable for real-time scenarios such as autonomous vehicles.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"I need to separate the vocals from a piece of music for a DJ performance. Find me an API that can do this.\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"separator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Open-Unmix model from PyTorch Hub to separate vocals, drums, bass, and other instruments in a pop music track. This model provides accurate music source separation tailored for DJ performances.', 'code': 'import torch\nseparator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)'}###"}
{"prompt":"Can you suggest an API that can be used to convert a recorded speech to text? I have recordings from meetings I want to be transcribed.\n\n###\n\n","completion":" {'domain': 'Speech-To-Text', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_stt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Speech-To-Text model from PyTorch Hub, which can transcribe recordings from meetings into text.', 'code': 'import torch\nmodel = torch.hub.load('snakers4\/silero-models', 'silero_stt', pretrained=True)'}###"}
{"prompt":"Recommend an API to analyze audio streams and detect when someone is speaking.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Voice Activity Detector (VAD) model from PyTorch Hub, which can analyze audio streams and detect speech segments.', 'code': 'import torch\nmodel, utils = torch.hub.load(repo_or_dir=\"snakers4\/silero-vad\", model=\"silero_vad\", force_reload=True)'}###"}
{"prompt":"Identify an API for recognizing objects and dividing them into different regions in given images.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet101 backbone from PyTorch Hub, which can recognize objects and divide them into different regions in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)'}###"}
{"prompt":"Help me find an API for segmenting objects in images using a pre-trained model for semantic segmentation.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet-50 backbone from PyTorch Hub for semantic segmentation of objects in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"An architect wishes to semantically segment different objects in an urban image. What API can they use for this purpose?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the DeepLabV3 model with MobileNet-V3 backbone from PyTorch Hub, pretrained on a subset of COCO and Pascal VOC datasets, to perform semantic segmentation on urban images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)'}###"}
{"prompt":"ABC news needs a text-to-speech service for their articles. Offer them an API.\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': 'model, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_tts', language=language, speaker=speaker)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Text-to-Speech model from PyTorch Hub to convert ABC news articles into natural-sounding speech in a single line of code.', '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)'}###"}
{"prompt":"Design a neural network that efficiently generates low-resolution images.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Deep Convolutional Generative Adversarial Networks (DCGAN) model from PyTorch Hub, designed for efficient generation of low-resolution images.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"Recommend an API suitable for classifying images of different dog breeds.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNext-101 32x8d WSL model from PyTorch Hub for classifying images of different dog breeds with high accuracy.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)'}###"}
{"prompt":"A researcher is looking for an image classification API that has been trained on a large dataset like ImageNet. Provide the API details for the ResNext WSL model.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub, which has been trained on ImageNet for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Find me an pretrained model for image classification that has scored 85% or more Top-1 accuracy on ImageNet.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt WSL model from PyTorch Hub, which has achieved a state-of-the-art Top-1 accuracy of 85.1% on ImageNet.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"I want an API to classify birds into different species using images. Can you provide me a code snippet to load a pre-trained model that can perform this task?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(\"facebookresearch\/WSL-Images\", \"resnext101_32x48d_wsl\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext-101 32x48d WSL model from PyTorch Hub, which can be fine-tuned for bird species classification based on given images.', 'code': 'import torch\nmodel = torch.hub.load(\"facebookresearch\/WSL-Images\", \"resnext101_32x48d_wsl\", pretrained=True)'}###"}
{"prompt":"I need an API that can classify images into 1000 different classes with high accuracy. How can create this?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 v1.5 model from PyTorch Hub, which comes with optimized performance and high accuracy for classifying images into 1000 different classes.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)'}###"}
{"prompt":"A designer wants to create an AI model that would generate thousands unique celebrity faces for his digital galleries. Present an API that can accomplish this.\n\n###\n\n","completion":" {'domain': 'Generative Adversarial Networks (GANs)', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub, which can generate high-quality celebrity faces.\", 'code': \"import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\"}###"}
{"prompt":"Sandra, a radiologist, needs to find abnormal regions in a brain MRI image quickly. Recommend an API that can automatically identify abnormal regions in a 3-channel brain MRI slice.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained U-Net model for brain MRI segmentation from PyTorch Hub. This model can identify abnormal regions in a 3-channel brain MRI slice from pre-contrast, FLAIR, and post-contrast sequences.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"Suggest me an API for classifying dog breeds from an image of a dog and output the top three likely breeds.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt101-32x4d model from PyTorch Hub, which you can fine-tune for dog breed classification. After fine-tuning, the model can output the top three likely dog breeds when given an image of a dog.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)'}###"}
{"prompt":"Recommend an API to classify objects in an image taken from the camera feed of a drone.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained SE-ResNeXt101-32x4d model from PyTorch Hub for classifying objects in an image taken from a drone camera feed.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"I need to classify objects in images with high accuracy. Can you recommend an API to use for this purpose?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-169 model from PyTorch Hub to classify objects in images with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"Snapchat is working on a new feature to enable their users to tag their pictures with predefined categories. Suggest a machine learning model that can classify an image into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained AlexNet model from PyTorch Hub for classifying images into different categories. This deep convolutional neural network can be easily applied to a variety of image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)'}###"}
{"prompt":"My application requires turning text into natural-sounding speech. What API can I use to achieve this?\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'You can use the pretrained WaveGlow model from NVIDIA via PyTorch Hub. It is a text-to-speech system that synthesizes natural-sounding speech from raw text.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)'}###"}
{"prompt":"I need to classify a picture of a cat among 1000 classes in ImageNet. Give me an API that can classify my cat picture.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the DenseNet-121 model from PyTorch Hub with pre-trained weights, which can classify images into 1000 classes, including cats.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"I am running a sentiment analysis on public opinion about a new social media app, could you recommend an API to classify the sentiments given a piece of text?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained RoBERTa model from PyTorch Hub, which can be fine-tuned for sentiment analysis of text data to understand public opinion about a new social media app.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)'}###"}
{"prompt":"Give me an API that can output the category of a car accident given a car accident dashcam footage.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet model from PyTorch Hub, which can be fine-tuned for car accident classification given a dashcam footage of the accident.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"Explain an API for street scene semantic segmentation using an RGB image and find the names of objects from the segmentation model.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet50 model from PyTorch Hub for street scene semantic segmentation using RGB images. The model provides object names from the segmentation results.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)'}###"}
{"prompt":"We would like to do semantic segmentation on a given image. Provide an API that can perform this functionality using the Fully Convolutional Network with ResNet101 backbone.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet101 model from PyTorch Hub for semantic segmentation.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"A photographer needs a tool for tagging various types of images. Recommend an API that would be suitable for classifying images into different categories.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained HarDNet model for Image Classification from PyTorch Hub to efficiently classify images into various categories by minimizing computational and memory access costs.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)'}###"}
{"prompt":"I work at a Tesla factory, and I need to find an AI that can classify whether the assembly of the Model 3 is of good quality or not. What should I use?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-161 model from PyTorch Hub, which can be fine-tuned to classify the assembly quality of Tesla Model 3.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)'}###"}
{"prompt":"I am developing a mobile app that identifies objects within images. Suggest an API that is efficient in terms of memory and computation for image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the HarDNet-85 model from PyTorch Hub, which is a low memory traffic and computationally efficient model designed for image classification.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"I am building a recommendation system for clothing and need an image classification API for detecting types of clothing. Can you suggest one?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68 model from PyTorch Hub, which can be fine-tuned for clothing type classification based on images.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"I want to build an application for image categorization using minimum resources such as memory and computational speed. Suggest me an API that serves my purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(\\'huawei-noah\/ghostnet\\', \\'ghostnet_1x\\', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained GhostNet model from PyTorch Hub which is designed for efficient image classification using minimal resources like computational speed and memory.', 'code': 'import torch\nmodel = torch.hub.load(\\'huawei-noah\/ghostnet\\', \\'ghostnet_1x\\', pretrained=True)'}###"}
{"prompt":"A content curator wants to automatically classify images to better organize their media library. Recommend an API that can help classify images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GoogLeNet model from PyTorch Hub to classify images into different categories.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\"}###"}
{"prompt":"I am working on a project that requires classifying thousands of images into different categories. Suggest an API that can identify objects within the images for my task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model for object classification from PyTorch Hub to classify images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"I am conducting research on cross-domain image classification. Can you suggest an API that offers a pretrained model that considers domain\/appearance invariance?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-101-IBN-a model from PyTorch Hub to perform cross-domain image classification. This model is designed with domain\/appearance invariance.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"I work for a camera security company, and I need a deep learning model to interpret and recognize person\/vehicle in live streaming CCTV. Which API should I use?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained IBN-Net model from PyTorch Hub, specifically the SE-ResNet-101-IBN-a variant, which is suitable for person\/vehicle re-identification tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"I need to classify photos of different human faces taken from different places. Can you provide me an API that can help with that?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a model from PyTorch Hub, which is designed for domain\/appearance invariance and is suitable for classifying human faces taken from different locations.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"The AI team at Snapchat is looking for a model to classify objects using images. Recommend a suitable API for this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained Inception v3 model from PyTorch Hub. This model is great for image classification and has achieved outstanding results on the ImageNet dataset.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)'}###"}
{"prompt":"Prepare an API to classify objects in an image using a model with split-attention networks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt101 model from PyTorch Hub, a ResNet variant with split-attention networks, for image classification.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"I need an image classifier based on a new ResNet variant to classify images according to the trained categories. Which API should I use?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt50 model from PyTorch Hub for image classification. This is a ResNet variant with Split-Attention Networks.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"A real-estate company is classifying homes using images taken by a drone. Suggest an API for classifying images of homes.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-269 model from PyTorch Hub to classify images of homes for the real-estate company.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)'}###"}
{"prompt":"Identify a deep learning model that can efficiently be used for image classification on a mobile device.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the ProxylessNAS Mobile model from PyTorch Hub, which is optimized for mobile devices with similar accuracy as other deep learning models.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"A wildlife photographer wants to classify animal species in his photos. Please recommend a machine learning API for this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model from PyTorch Hub, which is suitable for image classification tasks including identifying animal species in photographs.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"mobilenet_v2\", pretrained=True)'}###"}
{"prompt":"I need to classify the objects in the image using an API that is optimized for the GPU. Can you suggest one?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ProxylessNAS model with pre-trained weights from PyTorch Hub, optimized for GPU. This neural architecture search directly optimizes the task and offers a significant performance boost while maintaining accuracy.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"I want to create an app that can classify images taken by phone cameras. Recommend me an API that is optimized for mobile devices.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ProxylessNAS Mobile model from PyTorch Hub, optimized for mobile devices to perform image classification tasks on phone cameras.\", 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"I want to classify images of dogs into different breeds using a pre-trained deep learning model. Which API can I use?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use the pre-trained ResNet model from PyTorch Hub, which can be fine-tuned for dog breed classification.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)'}###"}
{"prompt":"Tell me how to create a code for identifying whether an image contains a dog or a cat.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from PyTorch Hub, which can be fine-tuned for binary classification to identify if an image contains a dog or a cat.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)'}###"}
{"prompt":"I need to categorize images into 1000 classes. Can you recommend an API for image classification?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-101 model from PyTorch Hub for image classification into over 1000 classes from ImageNet.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I am developing an app to label dog breeds found in images. Can you suggest an API that can be used to classify dog breeds?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load a pre-trained ResNet model for image classification from PyTorch Hub. Fine-tune this model on a dataset of dog breed images to classify the breeds.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"A mobile application developer wants to build a plant species identifier. Recommend an API that can easily classify plant species from images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext50 model from PyTorch Hub, which can be fine-tuned for plant species classification from images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnext50_32x4d\", pretrained=True)'}###"}
{"prompt":"Discuss an API that can classify images using lateral inhibition-inspired mechanisms and efficient backbones.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SNNMLP model from PyTorch Hub, which incorporates lateral inhibition-inspired mechanisms and efficient backbones for improved image classification accuracy.\", 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)'}###"}
{"prompt":"I want an API that can effectively distinguish between breeds of dogs using deep learning.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP base model from PyTorch Hub, which can be fine-tuned for dog breed classification.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)'}###"}
{"prompt":"Can you suggest an API that can classify images with the least number of parameters possible without losing much accuracy?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SqueezeNet model from PyTorch Hub, which can classify images with 50x fewer parameters than AlexNet and maintains a comparable level of accuracy.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\"}###"}
{"prompt":"I am building an image recognition software for my school project, Can you give me an API that will help me in recognizing the images I upload?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained ShuffleNet v2 model from PyTorch Hub, suitable for efficient image classification on the images you upload.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\"}###"}
{"prompt":"I am working on an app that can identify plants from images. Can you provide a good API for this task?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the SqueezeNet model provided by PyTorch Hub for image classification. The model can be fine-tuned with a dataset containing plant images to identify plants.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\"}###"}
{"prompt":"I want to recognize objects in images. Recommend an API for image recognition.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from PyTorch Hub for large-scale image recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"What is a suitable API that utilizes deep learning to detect and recognize hundreds of object categories in images?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11_bn model from PyTorch Hub, which can detect and recognize hundreds of object categories in images using deep learning.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"I have a bunch of images and I need a pre-trained deep learning model for image classification. Suggest an API for me to use.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained VGG13 model from PyTorch Hub, which is an effective model for large-scale image recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)'}###"}
{"prompt":"I have an image of an object and I want to classify the object in the image. Can you suggest an API that can help me with this task?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub for use in classifying the object in the image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I want an API to recognize different animals in a series of photos. Can you give me an example?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model with batch normalization from PyTorch Hub, which can be used to recognize animals in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)'}###"}
{"prompt":"I want to create an API that recognizes objects in an image. Recommend a pre-trained model that will be suitable.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub for image recognition.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"You are building a Chatbot for a car company that helps identify the model just by looking a picture. Provide an API that can help you recognize car models from a given image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load(repo_or_dir='pytorch\/vision', model='vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model from PyTorch Hub, which can be fine-tuned for car model recognition from a given image.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"vgg19\", pretrained=True)'}###"}
{"prompt":"I need to create an image classifier implementing Wide Residual Networks. Will you provide me an API call to accomplish this task using pre-trained weights?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Wide ResNet-50 model from PyTorch Hub with pre-trained weights for image classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)'}###"}
{"prompt":"A start-up company plans to develop media dashboard to monitor and categorize the latest YouTube trends. Which API should they use for video classification?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SlowFast model for video classification from PyTorch Hub to analyze and classify the action categories of YouTube videos.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)'}###"}
{"prompt":"Determine an API that can identify plant species from images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet101_2 model from PyTorch Hub, which can be fine-tuned to classify plant species from images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"Find me an API to use for video analysis in a project where we need to analyze football games.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained X3D model for video classification from PyTorch Hub to analyze football games and classify various actions performed by the players.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"Share with me a model to classify furniture pictures posted on an online store.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained OFA model from the MIT Han Lab for image classification. It has great performance across a wide range of devices and can be fine-tuned for furniture classification.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"Imagine a new photo album app that sorts photos by its theme. Find me an API that can classify images according to their theme.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', model_type=model_type, pretrained=True, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GPUNet model optimized for NVIDIA GPU performance from PyTorch Hub. This model can classify images into different categories based on their theme.', 'code': 'import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', model_type=model_type, pretrained=True, model_math=precision)'}###"}
{"prompt":"Identify an API that can analyze customer reviews and classify them as positive or negative.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\", 'api_provider': 'PyTorch', 'explanation': 'Load a pretrained BERT model from Hugging Face PyTorch-Transformers for sentiment analysis of customer reviews by classifying them as positive or negative.', 'code': 'import torch\nmodel = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'model\\', \\'bert-base-cased\\')'}###"}
{"prompt":"Design an API and example code that can classify optical, cable, and copper wires by images with accuracy.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet-18 model from Facebook Research's semi-supervised ImageNet models to classify images of optical, cable, and copper wires with high accuracy.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"Find me an API for neural machine translation that can translate English text to French.\n\n###\n\n","completion":" {'domain': 'Translation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': 'Load the Transformer model for neural machine translation from PyTorch Hub to translate English text to French.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\"}###"}
{"prompt":"Design an API to identify objects in an image quickly and efficiently.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Single Shot MultiBox Detector (SSD) model from PyTorch Hub for efficient and high-accuracy object detection in images.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"Come up with an API for our IVR system that generates natural-sounding spoken responses from provided text.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Tacotron 2 model from PyTorch Hub, which generates natural-sounding speech from text input, suitable for an IVR system.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math=\"fp16\")'}###"}
{"prompt":"I want to extract features from a given image using a pre-trained deep learning model. Can you provide me an API for this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V1','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 model from PyTorch Hub, which can be used for feature extraction and image classification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V1','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"An artist wants to claim their work in Reddit. Give an API that can identify their art from an uploaded picture.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub, which can be fine-tuned to identify an artist's artwork from an uploaded picture on Reddit.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"I want an API for classifying images into 1000 classes. What do you suggest?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3-Small 0.75 architecture from PyTorch Hub for classifying images into 1000 classes.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"An app developer wants to classify images of pets to make a pet-themed camera app. Suggest an API that can classify images of different pets.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL-V2 model for image classification from PyTorch Hub to classify images of different pets in a pet-themed camera app.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\"}###"}
{"prompt":"Please provide an API that can classify images of healthy foods to identify if it's vegan or not based on its ingredients.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load(repo_or_dir='szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 model from PyTorch Hub. This model can be fine-tuned for classifying food images into vegan or non-vegan based on the ingredients present in the image.', 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)'}###"}
{"prompt":"What is an API that can classify images into categories without using common tricks such as mixup, cutmix, or autoaugmentation?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model from PyTorch Hub for image classification. It achieves state-of-the-art performance without using common tricks such as architecture modification, outside training data, autoaugmentation, cosine learning rate, mixup\/cutmix, or label smoothing.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_100', pretrained=True)\"}###"}
{"prompt":"Help me to classify images in a Python project. What API should I use to achieve more than 80% top-1 accuracy on ImageNet without using any tricks?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained MEAL V2 model with more than 80% top-1 accuracy on ImageNet using the PyTorch framework. The MEAL V2 technique boosts classification accuracy without any tricks.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"Suggest me an AI model to classify animals given an image of a dog.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 image classification model from PyTorch Hub to classify animals from given images.', '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)\"}###"}
{"prompt":"I want an object detection API capable of handling object detection, drivable area segmentation, and lane detection for autonomous driving tasks.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub, which is specifically designed for autonomous driving tasks such as object detection, drivable area segmentation, and lane detection.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"Design an autonomous vehicle algorithm that can detect other vehicles, pedestrians, and the drivable area on the road. Suggest an appropriate API for this purpose.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the HybridNets model from PyTorch Hub with pre-trained weights, which can detect traffic objects, drivable area segmentation, and lane detection for autonomous vehicle applications.', 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"Calculate the relative inverse depth from a single input image. Is there an API that can do this?\n\n###\n\n","completion":" {'domain': 'Single Image Depth Estimation', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from the PyTorch Hub to compute the relative inverse depth from a single input image.', 'code': \"import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\"}###"}
{"prompt":"How can I estimate depth from a single image? Can you suggest an API for it?\n\n###\n\n","completion":" {'domain': 'Depth Estimation', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use the MiDaS model from PyTorch Hub to estimate depth from a single image. Use the 'DPT_Hybrid' model for high-quality depth estimation.\", 'code': \"import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\"}###"}
{"prompt":"A DJ wants to separate a song into four stems: vocals, drums, bass, and other instruments. What API can be used to achieve this?\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"model = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq')\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Open-Unmix model from PyTorch Hub to separate pop music into vocals, drums, bass, and other instruments.\", 'code': 'import torch\nmodel = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq')'}###"}
{"prompt":"Recommend an API that can convert a given text into speech with minimal dependencies.\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_tts', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Silero Text-To-Speech model from PyTorch Hub, which can convert a given text into natural-sounding speech with minimal dependencies.\", 'code': \"import torch\nmodel, symbols, sample_rate, example_text, apply_tts = torch.hub.load('snakers4\/silero-models', 'silero_tts', language='en', speaker='lj_16khz')\"}###"}
{"prompt":"My application requires voice activity detection for processing phone calls. Give me an API that detects speech segments in an audio file.\n\n###\n\n","completion":" {'domain': 'Audio Classification', 'api_call': \"model = torch.hub.load('snakers4\/silero-vad', 'silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Voice Activity Detector (VAD) model from PyTorch Hub to detect speech segments in an audio file.', 'code': \"import torch\nmodel, utils = torch.hub.load('snakers4\/silero-vad', 'silero_vad', force_reload=True)\"}###"}
{"prompt":"A drone startup requires an API to analyze aerial images to recognize roads, buildings, and trees.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet-50 backbone to perform semantic segmentation on aerial images, allowing the recognition of roads, buildings, and trees.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"How can I transcribe a recorded sales pitch from English speech to text using an API?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model, decoder, utils = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='en', device=device)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Speech-To-Text model from PyTorch Hub, which can transcribe a sales pitch from English speech to text.', 'code': 'import torch\ndevice = torch.device('cpu')\nmodel, decoder, utils = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='en', device=device)'}###"}
{"prompt":"I am working on a project that involves identifying bird species in pictures. Find me an API that can classify the bird species in an image.\n\n###\n\n","completion":" {'domain': 'Image classification', 'api_call': \"model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained NTS-Net model from PyTorch Hub, which is trained on the CUB-200 2011 dataset, to classify bird species in an image.\", 'code': \"import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\"}###"}
{"prompt":"A drone is flying over the neighborhood and is recording video. How can I process the video and perform semantic segmentation on each frame?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model for semantic segmentation from PyTorch Hub. Then, iterate over individual frames of the video to perform semantic segmentation on each frame.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\nmodel.eval()' }###"}
{"prompt":"I need to detect objects in a given image from a security camera. Can you provide me an API that can perform this task?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained YOLOv5 model from PyTorch Hub to perform object detection in a given security camera image.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"Recommend an API that can generate images of fashionable clothes and accessories automatically.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Deep Convolutional Generative Adversarial Networks (DCGAN) model from PyTorch Hub to generate images of fashionable clothes and accessories.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"Buzzfeed would like to classify images of their new articles into categories. Suggest an appropriate API to accomplish this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNext WSL model for image classification from PyTorch Hub to classify images related to Buzzfeed's articles into different categories.\", 'code': 'import torch\nmodel = torch.hub.load(\"facebookresearch\/WSL-Images\", \"resnext101_32x8d_wsl\", pretrained=True)'}###"}
{"prompt":"Help me label the objects in images so they can be used for an e-commerce platform. What AI algorithm can do that?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt WSL model from PyTorch Hub for image classification, which can identify objects in images for categorization in an e-commerce platform.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Help a startup to develop a recommendation system to identify plants in their garden that visitors can take pictures of, suggest an API that can be used for plant identification from a picture.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub, which can be fine-tuned for plant identification from images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"I am a developer at an online store, trying to classify the objects in the images provided for each product. Suggest an AI API which can be utilized for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained ResNeXt-101 32x48d WSL model from PyTorch Hub, which is trained on a large dataset for object classification and achieves state-of-the-art accuracy (85.4% top-1) on ImageNet.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)'}###"}
{"prompt":"Recommend me an API to classify images into different categories quickly.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained ResNeXt101-32x4d model from NVIDIA PyTorch Hub to classify images into different categories efficiently.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_resneXt\", pretrained=True)'}###"}
{"prompt":"Develop a solution to generate realistic high-quality faces. You can use a pre-trained GAN model as a starting point.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Progressive Growing of GANs (PGAN) model with pre-trained weights on the CelebA dataset to generate high-quality celebrity faces.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"I have brain MRI scans and I want to segment the abnormal regions. Recommend an API that can perform this task.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained U-Net model for brain MRI segmentation from PyTorch Hub to segment abnormal regions in brain MRI scans.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"Detect if a given pair of sentences about artificial intelligence has any contradiction., 'Input': 'Roberta is a heavily optimized version of BERT. Roberta is not very optimized.'\n\n###\n\n","completion":" 'contradiction'}}###"}
{"prompt":"Recommend an API to classify car models from images\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained AlexNet model from PyTorch Hub, which can be fine-tuned for classifying car models from images.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\"}###"}
{"prompt":"An automotive company is developing their own autonomous vehicle and they need a pretrained model to classify road signs. Propose an API that can be used for this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 v1.5 model from PyTorch Hub. This model can be fine-tuned to classify road signs for autonomous vehicles.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)'}###"}
{"prompt":"We are looking for an image classification model for our large datasets. Suggest an API that can classify images efficiently and accurately.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101-32x4d model from NVIDIA Deep Learning Examples PyTorch Hub. This model offers efficient and accurate image classification and is trained using mixed precision, providing faster results using Tensor Cores on Volta, Turing, and NVIDIA Ampere GPU architectures.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"My app needs a realistic human speech from text, point me to an API that helps achieve this.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': 'waveglow = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')', 'api_provider': 'PyTorch', 'explanation': 'Load the WaveGlow model from PyTorch Hub, which, in combination with Tacotron 2, can synthesize natural-sounding speech from input text.', 'code': 'import torch\nwaveglow = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')'}###"}
{"prompt":"I am creating a mobile app that identifies objects in images. Recommend an API for image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-121 model from PyTorch Hub for image classification with a dense convolutional network.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I need an API for categorizing images of flowers into their respective classes. Propose an API that can help me.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Densenet-201 model from PyTorch Hub, which can then be fine-tuned for flower classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"I want to classify images of different animals, give me an API that can help right now.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-169 model from PyTorch Hub to classify images of different animals.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"Suggest a deep learning API capable of categorizing images through an efficient neural network architecture.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the DenseNet-161 model from PyTorch Hub, known for its efficient neural network architecture, suitable for image classification tasks.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\"}###"}
{"prompt":"Suggest a suitable API to segment an image into semantically meaningful regions, such as people, cars, and buildings.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use PyTorch's FCN-ResNet50 model for semantic segmentation, which can separate an image into semantically meaningful regions like people, cars, and buildings.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)'}###"}
{"prompt":"For a city development project, I need to segment roads, buildings, and trees from satellite images. Propose an API that can help me with this task.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet101 model from PyTorch Hub for semantic segmentation. This model can be fine-tuned to segment roads, buildings, and trees from satellite images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"fcn_resnet101\", pretrained=True)'}###"}
{"prompt":"AppRocket wants a recommendation for a lightweight API to classify images for their mobile app. What is lightweight API that can use for this purpose?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the lightweight HarDNet-39DS model from PyTorch Hub for image classification in your mobile app.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"I am building an application that can recognize objects in images. Provide me with an API that can classify objects in an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-85 model from PyTorch Hub for image classification, which can recognize objects in images.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"Find an API that can help a mobile gallery app categorize images based on their content while having a low memory footprint.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet model, which is designed for low memory and computational footprint, suitable for mobile gallery app image categorization.', 'code': 'import torch\nmodel = torch.hub.load(\"PingoLH\/Pytorch-HarDNet\", \"hardnet68ds\", pretrained=True)'}###"}
{"prompt":"Obtain an API that can classify images using a lightweight neural network architecture.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GhostNet model from PyTorch Hub to classify images. GhostNet is efficient and lightweight, designed based on ghost modules that generate more features from cheap operations.\", 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\"}###"}
{"prompt":"What API could be used to identify plants in a garden from taken image?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GoogLeNet model from PyTorch Hub, which can be fine-tuned to identify plants in a garden using images.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\"}###"}
{"prompt":"I am planning to build a fashion blog which sorts numerous fashion trends. Find a proper API that can classify fashion styles from an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model from PyTorch Hub to classify fashion styles from images. The model can be fine-tuned to recognize specific fashion trends.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"I am working on a cross-domain image classification project, and I need a pre-trained model that has strong domain invariance. Can you recommend an API for this?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-IBN model from PyTorch Hub, which is specifically designed for domain invariance in image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"We are a project lead for a computer vision company and we are building a custom person re-identification system. Share with me an API that can be beneficial for cross-domain identification task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained IBN-Net model from PyTorch Hub that unifies instance normalization and batch normalization for cross-domain identification tasks specifically for person re-identification.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"I am looking for an API that can classify animals in images. Could you recommend a suitable API?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-50 model from PyTorch Hub, which can be fine-tuned for animal classification in images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"I need to accurately identify cross domain objects. Give me an API that can handle object recognition tasks with improved performance on cross domain tasks.\n\n###\n\n","completion":" {'domain': 'Object Recognition', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model (ResNeXt-101-IBN-a) from PyTorch Hub, which is especially suitable for cross-domain object recognition tasks as it combines instance normalization and batch normalization.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"I am maintaining a gallery and I am looking for a library that can classify images to different categories. Can you suggest an API?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the Inception v3 model available in the PyTorch Vision library for image classification into different categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)'}###"}
{"prompt":"Find an API that can efficiently classify dog breeds from the given photographs.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt101 model from PyTorch Hub, which can be fine-tuned for dog breed classification based on input images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"I want to classify images for my large-scale imaging project. Recommend an API to use for high accuracy image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-269 model from PyTorch Hub for high accuracy image classification.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Recommend an API that can optimize neural network architecture for efficient object classification on mobile devices.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ProxylessNAS model from PyTorch Hub, which is optimized for efficient object classification on mobile devices.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)'}###"}
{"prompt":"Provide me with an API capable of classifying images from a news website into Sports, Entertainment, World News, and Technology categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model for image classification from PyTorch Hub, and fine-tune it on a dataset with news categories such as Sports, Entertainment, World News, and Technology.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"We need a neural network for image classification and It should be specifically designed for different hardware platforms. Tell me an API that can help with this requirement.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model from PyTorch Hub, which specializes CNN architectures for different hardware platforms (CPU, GPU, Mobile) and provides a high performing image classification.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"I need to identify plant species from leaf images using a pretrained model. Recommend an appropriate API for this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained ResNet18 model from PyTorch Hub, which can be fine-tuned for plant species classification based on leaf images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)'}###"}
{"prompt":"Identify an API that can automatically detect objects in an image and return the object labels\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet34 model from PyTorch Hub, which can be fine-tuned for object detection and return object labels given an input image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"A programmer needs to classify images into 1000 categories like cats, dogs, planes, etc. Offer them an API that can perform this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNet50 model from PyTorch Hub, which is capable of classifying images into 1000 categories such as cats, dogs, planes, etc.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)'}###"}
{"prompt":"A startup needs to classify images using state of the art research. Suggest an image classification API to do this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet101 model from PyTorch Hub to classify images using state-of-the-art deep residual networks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)'}###"}
{"prompt":"A scientist wants to identify bird species by uploading photos. Propose an API that can classify bird species from an input image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet152 model from PyTorch Hub, which can be fine-tuned for bird species classification using input images.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"Suggest an API to develop a tool to classify plants based on their images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext model from PyTorch Hub, which can be fine-tuned for plant classification based on images.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\"}###"}
{"prompt":"You want an API to classify an image in order to organize your photos. Recommend an API that can be used for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext101 model from PyTorch Hub for image classification, which can be used to categorize and organize your photos.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)'}###"}
{"prompt":"Introduce an image classifier API that can predict the class of a given image for an app that tags photos according to their content.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP model for image classification from PyTorch Hub to predict the class of a given image.', 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\"}###"}
{"prompt":"Find me an AI framework that can classify various animals' images when given a picture file with a high accuracy.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP from the PyTorch Hub, which is a high-accuracy model for image classification, and can be used for classifying various animal images.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)'}###"}
{"prompt":"An app developer is searching for an AI solution to classify pets in photos. Recommend an API that can fulfill this need.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP model from PyTorch Hub. The SNNMLP model incorporates LIF neurons into an MLP-based architecture, with good accuracy and low computational requirements. This can be fine-tuned for pet image classification.', 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\"}###"}
{"prompt":"Make a recommendation for a pretrained API for an image classification task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Pretrained VGG11 model from PyTorch Hub can be used for image classification tasks.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)\"}###"}
{"prompt":"I need a fast and lightweight image classification model for a mobile app that can recognize objects in images. What API do you suggest?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet model from PyTorch Hub for lightweight and fast image classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)'}###"}
{"prompt":"Create a model that can categorize images into categories given its pretrained models.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet model from PyTorch Hub for image classification. It achieves AlexNet-level accuracy with fewer parameters and can be fine-tuned for custom image categorization tasks.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"squeezenet1_0\", pretrained=True)'}###"}
{"prompt":"CCTV cameras need to classify opened doors from closed ones. Suggest an image recognition API for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load a pre-trained VGG13 model from PyTorch Hub for image recognition to classify opened and closed doors.', 'code': 'import torch\nmodel = torch.h}###"}
{"prompt":"What is an API that can be used to classify images of different categories using a computationally efficient model?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load ShuffleNet v2, a pretrained, computationally efficient image classification model from PyTorch Hub that can classify images into different categories.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\"}###"}
{"prompt":"A student from MIT is working on a project that uses image recognition to identify the type of animal in an image. Give me an API that they can use to achieve this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model with batch normalization from PyTorch Hub, which can be used for animal image recognition.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"I want to get an API to recognize different types of animals in photographs, like lions, tigers, and bears.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model with batch normalization from PyTorch Hub, which can be fine-tuned for animal recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)'}###"}
{"prompt":"Identify an API that allows for image recognition with a high accuracy rate.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub for image recognition with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"Recommend an API that can be employed to identify the breed of a dog from a given image\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model with batch normalization from PyTorch Hub. You can fine-tune this model for dog breed identification.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\"}###"}
{"prompt":"Recommend an image recognition API that can identify the objects within a picture.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the VGG19 model from PyTorch Hub with pre-trained weights for image classification tasks. It can recognize objects within images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\nmodel.eval()' }###"}
{"prompt":"I have a photo and I want to know the object type in it. Can you provide an API that can recognize the object?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub to recognize objects in a given photo.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"Help me choose an adequate API to classify satellite images into land use categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-101-2 model from PyTorch Hub for satellite image classification into land use categories. The model can be fine-tuned for your specific task.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"A start-up is building a dog breed detector and they need an API that can classify images into categories. Suggest an ideal API that can help them achieve this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Wide ResNet 50_2 model from PyTorch Hub, which can be fine-tuned for dog breed classification from images.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\"}###"}
{"prompt":"I am developing a video indexing application and I want to classify actions in video clips. What API should I use?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': 'Load the pretrained X3D model from PyTorchVideo library for classifying actions in video clips based on the Kinetics 400 dataset.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"A racing company wants to optimize their code by using once-for-all networks. Suggest an API that can classify road traffic images with once-for-all networks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Once-for-all (OFA) model from PyTorch Hub for efficient image classification of road traffic images.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"Recommend an API for a pet food company that wants to make an app to identify cat breeds in pictures.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-18 model from the Facebook Research PyTorch Hub for image classification. The model has been trained with semi-supervised learning using the ImageNet dataset and can be fine-tuned to classify different cat breeds.', 'code': \"import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\"}###"}
{"prompt":"A travel buddy application is helping the tourists communicate at many non-english countries. Provide me an API which can help tourists translate simple questions like \"where is the restroom?\" when given the destination language code.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Transformer model for English-French translation from PyTorch Hub. This model can be used to translate simple questions from English to the target language, given the appropriate language code.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr')\"}###"}
{"prompt":"I am a product manager and I am building a recommendation system for my customers based on the pictures of the clothes they have purchased from our online store. Suggest an API that can classify the clothing type based on a given image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type='GPUNet-0', model_math='fp32')\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GPUNet model from PyTorch Hub, optimized for NVIDIA GPU performance, to classify clothing items based on images.\", 'code': 'import torch\nmodel_type = \"GPUNet-0\"\nprecision = \"fp32\"\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_gpunet\", pretrained=True, model_type=model_type, model_math=precision)'}###"}
{"prompt":"As a traffic analyst, I need an API that can detect vehicles from traffic camera images.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SSD (Single Shot MultiBox Detector) model pre-trained on the COCO dataset from PyTorch Hub, which can detect vehicles from traffic camera images.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"Can you tell me an API that can be used for image classification of flowers with high accuracy and efficiency?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet-B0 model from NVIDIA\\'s PyTorch Hub, which provides high accuracy and efficiency for image classification tasks, such as classifying flowers.', 'code': \"import torch\nefficientnet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"I need a PyTorch API for image classification that has been pretrained on ImageNet and achieves at least 80% top-1 accuracy.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MEAL_V2 model from PyTorch Hub, which has been pretrained on ImageNet and achieves over 80% top-1 accuracy.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"I need to classify an image using a state-of-the-art image classification model without any data augmentation techniques or additional tricks. What API should I use?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the MEAL V2 model from PyTorch Hub, which achieves state-of-the-art results without any data augmentation techniques or tricks.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I am an engineer in the environmental sector and I am attempting to develop an AI model that can accurately classify images of animals taken with a trail camera. Find an API that will support this capability.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model for image classification from PyTorch Hub. This model can help in accurately classifying images of animals taken with a trail camera.', 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)'}###"}
{"prompt":"I have an image classification task with limited computational resources. I need a lightweight image classification API recommendation for this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 MobileNet V3 Small 0.75 model from PyTorch Hub for lightweight image classification with limited computational resources.\", 'code': 'import torch\nmodel = torch.hub.load(\"szq0214\/MEAL-V2\", \"meal_v2\", model=\"mealv2_mobilenetv3_small_075\", pretrained=True)'}###"}
{"prompt":"Recommend an API that can analyze a video and classify different activities featured in the video.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained 3D ResNet model from PyTorch Hub to classify activities within a video.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"I want an API that can identify animals in a image from an ongoing research about endangered animals.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model with EfficientNet-B0 for image classification. Fine-tune the model to identify animals in an ongoing research about endangered animals.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"What API would help me estimate the depth of objects within a single image?\n\n###\n\n","completion":" {'domain': 'Depth Estimation', 'api_call': \"torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'The MiDaS API can be used to compute relative depth from single images. It provides different models suitable for various use cases, with its largest model, DPT_Large, offering the highest accuracy.', 'code': 'import torch\nmidas = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)'}###"}
{"prompt":"I want to build a self-driving car application that can detect objects, segment drivable areas, and detect lanes. Point me to an API that can do this.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub for object detection, drivable area segmentation, and lane detection that can be useful for self-driving car applications.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"I captured a photograph of a room and want to know the relative depth of the objects in it. Can you suggest an API which can help me with this?\n\n###\n\n","completion":" {'domain': 'Depth Estimation', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which can compute relative depth information from a single image of a room.', 'code': \"import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\"}###"}
{"prompt":"I have a pop music soundtrack and I want to extract drums, vocals, and bass from it. Give me an API that can perform this music separation.\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"model = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Open-Unmix model from PyTorch Hub, which is specifically designed for music source separation, allowing you to extract drums, vocals, bass, and other instruments from the soundtrack.', 'code': 'import torch\nmodel = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)'}###"}
{"prompt":"What is a good API for transcribing spoken language within a podcast into readable text in multiple languages?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_stt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained Silero Speech-To-Text model from PyTorch Hub, which supports multiple languages and can transcribe spoken language in a podcast into readable text.', 'code': 'import torch\nmodel, decoder, utils = torch.hub.load(\"snakers4\/silero-models\", \"silero_stt\", language=\"en\", device=torch.device(\"cpu\"))'}###"}
{"prompt":"What API should we use to perform semantic segmentation on aerial images for identifying buildings, roads, and vegetation?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with ResNet-50 backbone from PyTorch Hub for semantic segmentation tasks, such as identifying buildings, roads, and vegetation in aerial images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"I need an API that can detect voice activity from an audio file. Propose an API for this task.\n\n###\n\n","completion":" {'domain': 'Voice Activity Detection', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Silero Voice Activity Detector from PyTorch Hub to detect voice activity in an audio file.\", 'code': 'import torch\nmodel, utils = torch.hub.load(repo_or_dir=\"snakers4\/silero-vad\", model=\"silero_vad\", force_reload=True)'}###"}
{"prompt":"Create an audio version of an English text using an API.\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_tts', language='en', speaker='lj_16khz')\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Text-To-Speech model from PyTorch Hub to generate audio from an English text.', '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)\"}###"}
{"prompt":"Locate an API that could assist me in developing a street view application that highlights trees and buildings within the given images.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 with ResNet-101 model from PyTorch Hub, which can be used for semantic segmentation tasks, such as separating trees and buildings in street view images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)'}###"}
{"prompt":"I want to segment an image of Orchard Parks Shopping Center. Suggest an API for semantic segmentation that can segment items in a shopping center.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with MobileNet-V3 backbone from PyTorch Hub for semantic segmentation tasks, which can be utilized to segment items in a shopping center.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)'}###"}
{"prompt":"Get me an API to generate new clothing designs based on a database of existing clothes.\n\n###\n\n","completion":" {'domain': 'Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Deep Convolutional Generative Adversarial Networks (DCGAN) model from PyTorch Hub, capable of generating new clothing designs based on existing data.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"A security company wants to create a project that involves object detection from the live video feed captured by their CCTV cameras. Propose an API that provides object detection in real time.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': 'model = torch.hub.load(repo_or_dir='ultralytics\/yolov5', model='yolov5s', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5s model from PyTorch Hub for real-time object detection from live video feeds.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"I want to build an app for social media that filters profiles that have sunglasses in the picture. Give me an API that can detect presence of sunglasses within a picture.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub, which can be fine-tuned for detecting sunglasses within an image.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Suggest an API for automatically classifying images of road safety hazards such as potholes, damaged sidewalks, and obscured traffic signals for a road safety app.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt-101 32x8d WSL model from PyTorch Hub, which can be fine-tuned for road safety hazard classification.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)'}###"}
{"prompt":"Can you suggest an ML API capable of predicting whether a given image is a cat, dog or other animal with high accuracy?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the ResNext WSL model from PyTorch Hub, pretrained on ImageNet, which can be fine-tuned to classify images of cats, dogs, and other animals with high accuracy.\", 'code': \"import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\"}###"}
{"prompt":"Recommend an API suitable for an image recognition task to classify different species of animals.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt')', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt101-32x4d model from NVIDIA PyTorch Hub, which can be fine-tuned for classifying different animal species.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt')'}###"}
{"prompt":"A machine learning model is needed that can identify abnormalities in brain MRI scans. Recommend an API that could be used to train a model for this purpose.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained U-Net model for brain MRI segmentation from PyTorch Hub, which can be used to identify abnormalities in brain MRI scans.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"I want to create an album of generated paintings of famous landscapes. Provide me with an API that can generate such images.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub and fine-tune it for generating high-quality, realistic images of famous landscapes. The example code provided uses the model named \"celebAHQ-512\", but you can change this to a model trained on landscape images.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"Suggest an API for classifying a large dataset of images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained AlexNet model from PyTorch Hub for image classification on a large dataset.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)'}###"}
{"prompt":"Find me an API that can classify the images given corresponding image URLs. It should be fast and efficient.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 v1.5 model from NVIDIA via PyTorch Hub to perform fast and efficient image classification on image URLs.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)'}###"}
{"prompt":"I need to identify objects from a photo taken during a traffic scene, please provide me with an API call that can help in achieving this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the SE-ResNeXt101-32x4d model from PyTorch Hub for classifying objects in traffic scenes.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)'}###"}
{"prompt":"I'm working on an application that converts text into speech. Tell me an API that can serve my purpose.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': 'waveglow = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained WaveGlow model from PyTorch Hub, which works in conjunction with Tacotron 2 to synthesize natural-sounding speech from text.', 'code': 'import torch\nwaveglow = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)'}###"}
{"prompt":"Which pre-trained model should I use for sentiment analysis of customer reviews on our website?\n\n###\n\n","completion":" {'domain': 'Sentiment Analysis', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'You can use the RoBERTa large model from PyTorch Hub, which can be fine-tuned for the sentiment analysis task of customer reviews.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)'}###"}
{"prompt":"I need to classify various images into pre-defined categories. Suggest me an API that can be utilized for image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Dense Convolutional Network (DenseNet) model from PyTorch Hub for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"densenet121\", pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I am studying about vegetative cover in my college. Give me an API that can output segmented vegetation areas from a satellite image of a region.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained FCN-ResNet50 model from PyTorch Hub, which can be fine-tuned to segment vegetative cover from a satellite image.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)'}###"}
{"prompt":"I want to implement image classification based on DenseNet. Suggest an API that can classify images using DenseNet.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-161 model from PyTorch Hub to perform image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)'}###"}
{"prompt":"An e-commerce company is building a recommendation system that relies on object classification. Please suggest an API for this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use PyTorch Hub to load the pretrained DenseNet-201 model for image classification, which would be suitable for an e-commerce recommendation system.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"A machine learning engineer building an image recognition system that filters cats and dogs. Give me an API that can classify whether an image is a cat or dog.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Densenet169 model from PyTorch Hub. Fine-tune the model on a dataset of cat and dog images to classify whether an image contains a cat or a dog.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"I need an API to perform semantic segmentation on my images. Preferably a fully-convolutional network. What can you suggest?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet101 model from PyTorch Hub for semantic segmentation using a Fully-Convolutional Network with a ResNet-101 backbone.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)'}###"}
{"prompt":"Recommend an API that is suitable for detecting the type of currency in an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GoogLeNet model from PyTorch Hub, which can be fine-tuned for currency classification using images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)'}###"}
{"prompt":"Recommend a lightweight, efficient image classification model that can classify retail products by category.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the HarDNet-68 model from PyTorch Hub, a lightweight and efficient image classification model that can be fine-tuned for retail product classification by category.', 'code': 'import torch\nmodel = torch.hub.load(\\'PingoLH\/Pytorch-HarDNet\\', \\'hardnet68\\', pretrained=True)'}###"}
{"prompt":"I want a classifier that can recognize clothing items in images, taking into account the images may have different styles.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet-50-IBN-a model from PyTorch Hub, which incorporates instance normalization for better invariance to style variations in images.\", 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"Recommend an API that can classify different types of plants from their images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-85 model from PyTorch Hub, which can be fine-tuned for plant classification on images.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"We are building a phone app for Intel Corporation which can detect various objects in an image. Can you help me with an API which can classify objects into different categories for given image?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GhostNet model from PyTorch Hub, which can classify objects in an image into different categories with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)'}###"}
{"prompt":"An e-commerce company is looking for an API to identify products in images for better recommendations on their app. Can you recommend an API that can perform this task?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model from PyTorch Hub, which can be fine-tuned for product image classification tasks on your e-commerce app.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"Recommend an API best suited for an app that needs to recognize unknown persons in images\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net, which unifies instance normalization and batch normalization, suitable for cross-domain or person identification tasks.', 'code': \"import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\"}###"}
{"prompt":"Design an image classifier for a phone manufacturer to identify inappropriate images from user gallery and flag them.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-50 model from PyTorch Hub, which can be fine-tuned for inappropriate image classification using a custom dataset.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"Recommend an image classification API that performs state-of-the-art performance for a mobile app.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the ResNeSt-200 model from PyTorch Hub to get a state-of-the-art image classification model for your mobile app.\", 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Provide an API for classifying images of fruits in a retail store.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt101 model from PyTorch Hub, which can be fine-tuned for fruit classification in a retail store.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"Let's say I've created an app for birdwatchers to recognize birds in the images they take. Recommend me a high-performance image recognition API that would be ideal to use for my app.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Use the pretrained Inception v3 model from PyTorch Hub, which can be fine-tuned for bird species recognition. The model is trained on the ImageNet dataset and achieves high accuracy.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\"}###"}
{"prompt":"I am a Machine Learning Engineer at Pinterest and I need an API to classify images. Suggest an efficient API.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model for image classification from PyTorch Hub. It performs efficient classification of images using advanced ResNet-style architecture with split-attention.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)'}###"}
{"prompt":"I have a limited computational budget for image classification tasks. Suggest an API optimized for CPU performance.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model optimized for CPU performance from PyTorch Hub to perform image classification tasks with limited computational budget.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)'}###"}
{"prompt":"Can you recommend an API for a mobile app developer who wants lightweight and fast image classification on their app?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the ProxylessNAS Mobile model from PyTorch Hub, which is optimized for low-resource hardware platforms found on most mobile devices, ensuring fast image classification.\", 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"Develop an image classifier that can identify objects in an image. Provide an API example to load a pretrained classifier model.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-18 model from PyTorch Hub for image classification to identify objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)'}###"}
{"prompt":"I want to analyze the objects within my images taken from my phone to use it in my machine learning dataset, suggest me an API that is best suited for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model from PyTorch for lightweight and efficient image classification, particularly useful for processing images taken from your phone.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"How can a botanist distinguish between different plant species using an API based on deep residual networks?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet34 model from PyTorch Hub, which can be fine-tuned for plant species classification using deep residual networks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"A new dating app wants to analyze its user's images to determine if the user is an authentic account or an impersonator. Give me an example of API that can help differentiate between real profile images and fake one.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model optimized for GPU from PyTorch Hub, which can be fine-tuned for determining the authenticity of user profile images.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"Many image web services need an efficient image recognition model. Can you provide an API that uses deep residual networks for image classification?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet50 model from PyTorch Hub to perform image classification using deep residual networks.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)'}###"}
{"prompt":"I'm designing an automatic image moderation system for my social platform. Recommend an API for classifying images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet101 model from PyTorch Hub for image classification. It's a deep residual network trained on ImageNet and is capable of classifying images into different categories.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)'}###"}
{"prompt":"I have a set of pictures of birds and I want to put them in categories based on their species. What API can I use to classify these images based on their content?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext50 model from PyTorch for image classification, which can be fine-tuned to classify bird species from images.', 'code': 'import torch\nfrom PIL import Image\nfrom torchvision import transforms\n\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)'}###"}
{"prompt":"Can you tell me about an API that can give me powerful image classification capabilities for a mobile app?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Small model from the PyTorch Hub, suitable for mobile applications due to its low computational cost and high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"Let's use a neural network to classify a given image. Recommend an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Tiny model from PyTorch Hub for image classification, incorporating Leaky Integrate-and-Fire (LIF) neurons to achieve better accuracy with fewer FLOPs.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)'}###"}
{"prompt":"I work at a camera manufacturing company, and we want an API that will help our camera to identify and classify images. Ideally, the API should be based on deep learning techniques.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP model from PyTorch Hub with pre-trained weights for image classification. The model is based on deep learning techniques, and can identify and classify images.', 'code': 'import torch\nmodel = torch.hub.load(\"huawei-noah\/Efficient-AI-Backbones\", \"snnmlp_b\", pretrained=True)'}###"}
{"prompt":"In the process of automating image classification for a product recognition tool, provide me with an API that can effectively sort categorized images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SqueezeNet model from PyTorch Hub for efficient image classification, providing AlexNet-level accuracy with significantly fewer parameters to organize categorized images.\", 'code': 'import torch\nmodel = torch.hub.load(\\'pytorch\/vision\\', 'squeezenet1_0', pretrained=True)'}###"}
{"prompt":"\"An image from my friend's vacation album needs to be categorized without any further information. Give me an API that can classify the content of the image.\"\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet1_1 model from PyTorch Hub to classify the content of an image with minimal input information.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\nmodel.eval()\"}###"}
{"prompt":"A smartphone company wants to implement image classification for their mobile camera app. Provide an API that can identify objects in a given image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ShuffleNet v2 model from PyTorch Hub for fast and efficient image classification for identifying objects in a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)'}###"}
{"prompt":"Develop an API for classifying images of cars into brands based on a pretrained model.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model with batch normalization from PyTorch Hub, which can be fine-tuned to classify car images into different brands.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"Recommend an API that can help a wildlife photographer to identify animals from their photos.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG13 model from PyTorch Hub to assist a wildlife photographer in identifying animals from their photos.\", 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"vgg13\", pretrained=True)'}###"}
{"prompt":"I am a programmer who is interested in building a photo application that recognizes plants using camera feed. Recommend me an API and some sample code to achieve this.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use VGG13-BN model from PyTorch Hub with pretrained weights for large-scale image recognition tasks. Fine-tune or add additional training data to recognize plants from camera feed.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Recommend an API that can be used to build an image recognition app for automating car insurance claim process.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model with batch normalization from PyTorch Hub for image recognition tasks in automating car insurance claim processes.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)'}###"}
{"prompt":"I want to recognize objects present in a given image. Is there an API that can help me in this task?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub, capable of recognizing objects in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"Recommend an API that can recognize objects in photos, specifically the VGG19 model, and provide code to load the pretrained version of the model.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model from the PyTorch vision library for object recognition in images.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\nmodel.eval()\"}###"}
{"prompt":"Propose a model that can be used to identify different bird species from images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load(\"pytorch\/vision\", \"wide_resnet50_2\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-50-2 model from PyTorch Hub, which can be fine-tuned to identify different bird species from images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"wide_resnet50_2\", pretrained=True)'}###"}
{"prompt":"An android developer is building an application with image recognition features for museum visits. Find an API so that they can classify images taken during those visits.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained Wide ResNet-101-2 model from PyTorch Hub to classify images of objects taken during museum visits.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Can you suggest an API to help identify different physical activities from short video clips?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained X3D model from PyTorch Hub for classifying short video clips into different physical activities.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"Find an API that works with street camera videos and classifies the type of actions happening in the video.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': \"Load the SlowFast Networks model from PyTorchVideo Hub with pre-trained weights for video classification, which is capable of classifying actions in street camera videos.\", 'code': \"import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\"}###"}
{"prompt":"I am a developer at OpenAI and I am building a small-scale chatbot that can conduct conversation in English text input. Can you find me an API that is suitable for the job?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\", 'api_provider': 'PyTorch', 'explanation': 'Load the BERT model from Hugging Face PyTorch-Transformers, which can be used for various NLP tasks, including sequence classification and question answering, making it suitable for building a chatbot.', 'code': 'import torch\nmodel = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')'}###"}
{"prompt":"A foundation wants to automatically categorize plant species from leaf images taken in a field. Give me an API for classifying different types of plant species based on identification and characterization of leaves.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Once-for-all (OFA) Networks model from PyTorch Hub, which can be fine-tuned for plant species classification based on leaf images.', 'code': \"import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)\"}###"}
{"prompt":"A research team is building a new categories labels system for an image dataset. What is an API that can do image classification for them?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained semi-supervised and semi-weakly supervised ImageNet model from PyTorch Hub, which can classify images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"A team needs an image classifier for building a media cataloguing system optimized for NVIDIA hardware. Give me an API using GPUs that can classify images into categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GPUNet model from NVIDIA Deep Learning Examples PyTorch Hub to classify images using NVIDIA GPUs for optimized performance.', 'code': 'import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)'}###"}
{"prompt":"I need an API for translating an English paragraph to French, perfect candidate should support large-scale semi-supervised training and back-translation.\n\n###\n\n","completion":" {'domain': 'Neural Machine Translation', 'api_call': 'model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Transformer model from PyTorch Hub, which supports English to French translation, large-scale semi-supervised training, and back-translation.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')'}###"}
{"prompt":"Design an image recognition software for identifying food items on social media. Suggest an API with pretrained model for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet-B0 model from NVIDIA Deep Learning Examples repository, which can be further fine-tuned to identify food items in images.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"I am trying to implement a drone surveillance system with intruders detection using a camera system. Suggest me an API capable of performing object detections for my drone's inbuilt camera.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SSD (Single Shot MultiBox Detector) model from PyTorch Hub for object detection with your drone's camera system.\", 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"I am working on a text-to-speech project for my company. Can you suggest an ML API to convert text into human-like speech?\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Load the Tacotron 2 model from PyTorch Hub, which generates natural-sounding speech from text without any additional prosody information.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_tacotron2\", model_math=\"fp16\")'}###"}
{"prompt":"Generate classified output using the pretrained MEAL V2 model for object classification for an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub to classify objects within an input image using ensemble knowledge distillation via discriminators.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"We are developing an image recognition system for our security camera at the entrance of our building. What kind of API is suitable for this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub, which is suitable for image recognition tasks like security camera image classification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I'm working on an app for animal identification from images, and I need an API to classify animal species from images. Suggest an API for that.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model on PyTorch Hub, which is an image classification model with excellent performance that can be used to classify animal species from images.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"Provide an API that can identify objects in an image with an accuracy of 80% or higher on the ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3-Small architecture from PyTorch Hub, which has a top-1 accuracy of 67.60% and can be further fine-tuned to achieve 80% or higher accuracy on the ImageNet dataset.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"Describe an API specialized for image classification without employing known common tricks. Include the API repository and model name.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the MEAL V2 pretrained model for image classification without common tricks. The API repository is 'szq0214\/MEAL-V2' and the model name is 'mealv2_mobilenetv3_small_100'.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_100', pretrained=True)\"}###"}
{"prompt":"A deepfake inspector company would like to classify the authenticity of a video. Suggest me an API that will classify if the video is deepfake or original.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained 3D ResNet model from PyTorch Hub, which can be fine-tuned for deepfake video classification to determine if a video is original or manipulated.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"Find me a state-of-the-art pretrained image classifier from a model that does not rely on additional tricks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MEAL V2 pretrained model, which has achieved state-of-the-art performance on ImageNet without using additional tricks.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"I want to create an app that recognizes dog breeds in images. Please help me identify a suitable API for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model with MobileNet V3-Large in PyTorch Hub to recognize dog breeds in images. The model achieves high accuracy without common tricks, making it suitable for your application.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"Obtain information on a machine learning API capable of detecting objects, drivable areas, and lanes in traffic images, such as those from a self-driving car's camera feed.\n\n###\n\n","completion":" {'domain': 'Object Detection and Segmentation', 'api_call': 'model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HybridNets model from PyTorch Hub, which is designed for traffic object detection, drivable area segmentation, and lane detection in traffic images.', 'code': 'import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)'}###"}
{"prompt":"I work for a self-driving car company and we need an API to perform simultaneous object detection, drivable area segmentation, and lane detection in images taken from vehicle cameras.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub, which can perform object detection, drivable area segmentation, and lane detection simultaneously in real-time.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"I want to get the relative depth information from a single image taken in a forest.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model (DPT_Large) from PyTorch Hub to compute relative depth information from a single forest image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)'}###"}
{"prompt":"As a game developer working on a VR simulation, I want a technology that can predict relative depth of an object in an image. Provide me with a suitable API to achieve this.\n\n###\n\n","completion":" {'domain': 'Depth Estimation', 'api_call': 'model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the MiDaS model from PyTorch Hub for estimating relative depth from a single image that can be used in your VR simulation.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)'}###"}
{"prompt":"Let's say I have an image of a cityscape and I want to determine the relative depth of different objects within the image. Which API can help me do that?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the MiDaS model from PyTorch Hub, which has been pretrained to compute relative inverse depth from a single image, useful for cityscapes.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"Identify an API that can transcribe a voice message to written text.\n\n###\n\n","completion":" {'domain': 'Speech-to-Text', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='en', device=device)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Speech-To-Text pre-trained model to transcribe voice recordings to text.', 'code': 'import torch\ndevice = torch.device('cpu')\nmodel, _, _ = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='en', device=device)'}###"}
{"prompt":"A wildlife photographer wants to identify bird species from his bird photos. Suggest an API capable of recognizing bird species from an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NTS-Net model for bird species classification from PyTorch Hub to recognize bird species from an image.', 'code': 'import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})'}###"}
{"prompt":"Identify the segments with speech from an audio file using an API.\n\n###\n\n","completion":" {'domain': 'Voice Activity Detection', 'api_call': \"model = torch.hub.load('snakers4\/silero-vad', 'silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Voice Activity Detector model from PyTorch Hub to identify segments containing speech in an audio file.', 'code': \"import torch\nmodel = torch.hub.load('snakers4\/silero-vad', 'silero_vad', force_reload=True)\"}###"}
{"prompt":"I want to build a robot that recognize objects in the street. Provide me an API that can segment objects in an image.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained DeepLabV3 model with the ResNet-50 backbone from PyTorch Hub for semantic segmentation of objects in street images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"I need to convert text to speech mp3 format with natural sounding voices. Can you find me an API that can do that?\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_tts', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Text-To-Speech model from PyTorch Hub to convert text into natural-sounding speech.', '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)'}###"}
{"prompt":"Our company wants to segment images into multiple classes. Suggest an API for semantic segmentation.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a ResNet-101 backbone from PyTorch Hub to perform semantic segmentation on images, dividing them into multiple classes.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)'}###"}
{"prompt":"I need a semantic segmentation model to segment different objects present in road scene images. Suggest me an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with MobileNet-V3 backbone from PyTorch Hub, which is suitable for segmenting different objects in road scene images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)'}###"}
{"prompt":"What API can I use to detect objects in an image while maintaining a small model size for a mobile application?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5s model from PyTorch Hub, which is a small and efficient object detection model suitable for mobile applications.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"I am designing clothing for a new fashion line and need inspiration. Provide an API that can generate clothing images.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Deep Convolutional Generative Adversarial Network (DCGAN) model from PyTorch Hub to generate low-resolution images of clothing for inspiration.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"I need an API that can classify a given image into one of the 1000 classes like cars, dogs or flowers.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNext WSL (Weakly Supervised Learning) model from PyTorch Hub for classifying images into one of 1000 classes.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"I want to classify images using a high-performance machine learning model. Point me to an API that can help achieve this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext-101 32x16d WSL model from PyTorch Hub to classify images with state-of-the-art accuracy when fine-tuned on the ImageNet1K dataset.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Design an API that can classify images into different categories using a pretrained model.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub to classify images into 1000 different categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)'}###"}
{"prompt":"I am working on an app that generates unique user avatars. I need an API that creates realistic images of faces. Provide me with the API details and pre-trained model if available.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': \"Use the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub to generate high-quality, realistic images of human faces.\", 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', model_name='celebAHQ-512', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"A research team is analyzing brain MRI images for abnormality segmentation. Suggest a suitable API to achieve this.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained U-Net model for biomedical image segmentation from PyTorch Hub to segment abnormalities in brain MRI images.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"Jane is developing a mobile app to identify animal species. Propose an API that classifies animals based on images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 v1.5 model from PyTorch Hub, which can be fine-tuned for animal species classification using images.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_resnet50\", pretrained=True)'}###"}
{"prompt":"Find me an API to classify objects in images given a massive dataset of real life images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained AlexNet model from PyTorch Hub, which can be used for object classification in real-world images from a large dataset like ImageNet.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\"}###"}
{"prompt":"I want to classify images in real-time during a robotics competition. Recommend an API for image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt101-32x4d model from the NVIDIA PyTorch Hub repository for real-time image classification during a robotics competition.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\"}###"}
{"prompt":"A photographer at National Wildlife Foundation wants to automatically classify and organize the wildlife photos he has captured. Suggest an API that can classify animal images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101-32x4d model from PyTorch Hub, designed for ImageNet classification, which can be fine-tuned to classify wildlife photographs into appropriate categories.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)\"}###"}
{"prompt":"We need to develop a robotic assistant, and it needs to be able to speak given a text input. Provide a text-to-speech API that can convert a text input into natural-sounding speech.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the WaveGlow model, available from PyTorch Hub, to synthesize natural-sounding speech from an input text.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\"}###"}
{"prompt":"Find me an API that can determine the sentiment of a given text review.\n\n###\n\n","completion":" {'domain': 'Sentiment Analysis', 'api_call': 'model = torch.hub.load('pytorch\/fairseq', 'roberta.large.mnli', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained RoBERTa model for natural language inference from PyTorch Hub to predict the sentiment of a given text review.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'roberta.large.mnli', pretrained=True)'}###"}
{"prompt":"I have a collection of photos of different objects and want to classify them. Suggest me an API that can help in this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-121 model from PyTorch Hub, which is suitable for classifying images of different objects.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"We need a deep learning model for image classification that is less computationally expensive and has a low number of parameters while maintaining the accuracy performance. Provide me an API for image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-169 model from PyTorch Hub, which has a low number of parameters, is less computationally expensive, and achieves good accuracy for image classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"Design an image classifier that can differentiate between various classes. Recommend an API that can serve this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained DenseNet-201 model from PyTorch Hub for image classification across multiple classes.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"Suggest a suitable API that can classify images into different categories based on ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-161 model from PyTorch Hub to classify images into different categories based on the ImageNet dataset.', 'code': 'import torch\nmodel = torch.hub.load(\\'pytorch\/vision\\', \\'densenet161\\', pretrained=True)'}###"}
{"prompt":"Recommend an API for segmenting objects in images for an autonomous vehicle project.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Fully-Convolutional Network (FCN) model with a ResNet-50 backbone from PyTorch Hub for semantic segmentation, suitable for an autonomous vehicle project.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)'}###"}
{"prompt":"I need an API that generates semantic segmentation of an input image taken by a self-driving car.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained FCN-ResNet101 model from PyTorch Hub for semantic segmentation of an input image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)'}###"}
{"prompt":"Weddings and events are photographed every day. What API can help me label these photos by category for my wedding business?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet model from PyTorch Hub for image classification. This model is efficient and can help label your wedding photos by category.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"Discover an efficient API that can classify the types objects in an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-85 model from PyTorch Hub, an efficient image classification model with low memory traffic.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I am creating a mobile app to identify plants. I need an API that has a lighter-weight model for image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HarDNet-68 model from PyTorch Hub, which is a lightweight model with low memory traffic, suitable for mobile apps and can be fine-tuned for plant identification.\", 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"Recommend an API that can classify images into different categories using a model designed by Google.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GoogLeNet model for image classification from PyTorch Hub, which was created by Google.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)'}###"}
{"prompt":"I want to build a mobile application that can classify an object based on its image. Recommend a lightweight API that can provide high performance on mobile devices.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GhostNet model from PyTorch Hub, a lightweight and efficient architecture for image classification, suitable for use on mobile devices.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)'}###"}
{"prompt":"In a new computer vision project, we need a model that can classify images into multiple possible categories. Recommend a suitable pre-trained model.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a model from PyTorch Hub, which can classify images into multiple categories using IBN-Net for domain\/appearance invariance.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"An AI engineer is developing an image recognition system for Intel Corporation. Provide me an API to classify images using IBN-Net model.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-101-IBN-a model from PyTorch Hub to classify images using the IBN-Net model, which is suitable for cross-domain image recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Looking for something to help re-identify a person from diferent photos. Find me an API for that.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt-101-IBN-a model from PyTorch Hub, which is suitable for person re-identification tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"Can you provide an API that could identify different types of cars given an image of the scene?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the Inception v3 model from PyTorch Hub with pre-trained weights. This model can be fine-tuned to identify different types of cars given an input image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)'}###"}
{"prompt":"I need to classify different types of animals in images taken from the wild.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the ResNeSt-200 model from PyTorch Hub with pre-trained weights for image classification of different types of animals.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)'}###"}
{"prompt":"We need to implement an image classification model for our AI platform. Can you suggest an appropriate API we can use as a base?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNeSt-101 model from PyTorch Hub, which offers excellent image classification performance and uses the Split-Attention Networks architecture.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"Provide an API to classify buildings to residential, commercial or industrial based on their locations from images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-50 model from PyTorch Hub, which can be fine-tuned for building classification into residential, commercial or industrial.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"I work at a tech company and I need to identify dog breeds in images. Provide me with an API that can classify dog breeds based on an input image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt269 model from PyTorch Hub, which can be fine-tuned for dog breed classification.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)'}###"}
{"prompt":"Create a mobile app to detect objects. Recommend me an API that would ideally suit this purpose.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ProxylessNAS model optimized for mobile devices from PyTorch Hub, which can be fine-tuned for object detection in a mobile app.\", 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"I need an API that can classify objects in images and has a lightweight model suitable for mobile devices.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model from PyTorch Hub, which is designed for efficient image classification on mobile devices.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"Develop a mobile application that can classify objects in images captured by the phone camera. Suggest an API that can do the classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model optimized for mobile devices from PyTorch Hub to classify objects in images captured by the phone camera.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"To help an online store to label images taken from the deep sea, pick the appropriate API for this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from PyTorch Hub, which can be fine-tuned to classify images taken from the deep sea.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnet50\", pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Suppose you work as a data scientist in IMB and you need an image classifier for your project on animals. Can you provide me the code that loads a model for image classification?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNet18 model from PyTorch Hub for image classification, which can be fine-tuned for an animal classification task.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\nmodel.eval()' }###"}
{"prompt":"I am organizing my photo library and need an API to identify what's in each photo, can you make a code for me?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet34 model from PyTorch Hub for image recognition and use it to identify the contents of your photo library.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"An application is being built that can identify different vehicles in photos. Suggest an API that can be used for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet101 model from PyTorch Hub, which can be fine-tuned for vehicle classification in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)'}###"}
{"prompt":"I need an API for image classification that employs deep residual networks. Can you provide me with one?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ResNet152 model from PyTorch Hub, which is a deep residual network pretrained on ImageNet, suitable for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"A photographer at Adobe Stock wants to automatically tag their large photo inventory using AI. Point them to an ready-to-use API that accomplishes this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext model for image classification from PyTorch Hub to automatically tag images based on their content.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnext101_32x4d\", pretrained=True)'}###"}
{"prompt":"To create an app that tells a user if the food they take a photo of is safe to eat, provide me with an appropriate API that can classify images of food products.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext50_32x4d model from PyTorch Hub, which can be fine-tuned for food safety classification given an image of a food product.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)'}###"}
{"prompt":"Find me an API capable of image classification based on Spiking Neural Networks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Base model from PyTorch Hub, which is an image classification model based on Spiking Neural Networks for efficient and accurate classification.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)'}###"}
{"prompt":"What is an API that can be provided to a person working on the Hulu app for Apple TV to classify images?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained SNNMLP model from PyTorch Hub to perform efficient image classification with a high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)'}###"}
{"prompt":"An e-commerce website wants to integrate an AI assistant to differentiate between various items being uploaded by sellers. Give me an API to classify the uploaded items based on their image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Small model from PyTorch Hub, which can perform image classification on the uploaded items, allowing the AI assistant to differentiate between various products.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"An NGO ingin Gitonga is building an AI bot that identifies trees using images from the Amazon rainforest. Help me find an API that can classify images of trees using pre-trained models.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet model from PyTorch Hub, which serves as a base for image classification and can be fine-tuned to classify trees from the Amazon rainforest.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)'}###"}
{"prompt":"I want a lightweight PyTorch API for object recognition for my application. What would you suggest?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from PyTorch Hub, which is a lightweight option for object recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"I want to categorize images from a dataset with pretrained API. Give me an ideal choice.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG11 model with batch normalization from PyTorch Hub for large-scale image recognition and classification.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"I am a researcher in the field of computer vision, and I need to classify images using a pre-trained model with good performance. Can you give me an API for that?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model from PyTorch Hub for image classification tasks. VGG models are widely used for large-scale image recognition tasks and have good performance.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)'}###"}
{"prompt":"I need a PyTorch API to recognize various objects in images for my project. Can you provide one?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub, which can be used for object recognition in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"Can you provide me with an API capable of image recognition for my autonomous vehicle project?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the VGG16 model with batch normalization from the PyTorch Vision library for image recognition tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)'}###"}
{"prompt":"Describe an API that can help identify objects and scenes in a series of photos from a vacation.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model from PyTorch Hub, which can be used for large-scale image recognition tasks to identify objects and scenes in vacation photos.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)'}###"}
{"prompt":"I need an API that can recognize the dog breed from a given image of a dog.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub, which can be fine-tuned for dog breed recognition from a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"A computer vision company needs an efficient image recognizer API for a security project. Find a suitable API to classify objects in an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Wide ResNet50_2 API from PyTorch Hub, which is pretrained for object recognition using the ImageNet database.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"wide_resnet50_2\", pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Provide a pretrained API that can identify and classify dogs in images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-101-2 model from PyTorch Hub, which can be used for image classification tasks such as identifying and classifying dogs.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"A company called \"VideoTag\" wants to classify videos into different categories using machine learning. Provide an API that can help them with video classification.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SlowFast model for video classification from PyTorch Hub to categorize video clips into various action classes.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)'}###"}
{"prompt":"Recommend an API that can classify images quickly and efficiently using an NVIDIA GPU.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Load the NVIDIA GPUNet model from PyTorch Hub, which is designed to classify images quickly and efficiently using an NVIDIA GPU.', 'code': 'import torch\nmodel_type = \"GPUNet-0\"\nprecision = \"fp32\"\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_gpunet\", pretrained=True, model_type=model_type, model_math=precision)'}###"}
{"prompt":"A news aggregator wants to categorize and summarize the news article. Which API they can use for text classification and understanding?\n\n###\n\n","completion":" {'domain': 'Natural Language Processing', 'api_call': 'model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained BERT model for text classification and understanding from PyTorch Hub to categorize and summarize news articles.', 'code': 'import torch\nmodel = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased)'}###"}
{"prompt":"A fintech company wants to build a model that can identify the pattern on a credit card. What API can they use to achieve this classification?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained Once-for-all (OFA) network from PyTorch Hub to classify patterns on credit cards. This model is efficient and provides excellent performance for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True).eval()'}###"}
{"prompt":"What is an API to provide me with a pretrained image classifier that would recognize object types in images?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the ResNet-18 swsl model from Facebook Research via PyTorch Hub. This model is pretrained with semi-supervised and semi-weakly supervised ImageNet models and can recognize object types in images.\", 'code': 'import torch\nmodel = torch.hub.load(\"facebookresearch\/semi-supervised-ImageNet1K-models\", \"resnet18_swsl\", pretrained=True)'}###"}
{"prompt":"Provide me with a Machine Learning API that can classify objects in images with high accuracy and low computational cost.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NVIDIA EfficientNet-B0 model from PyTorch Hub, which offers high accuracy and low computational cost for image classification tasks.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"I need to translate a paragraph from English to French. What API would you recommend for this task?\n\n###\n\n","completion":" {'domain': 'Translation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': \"Use the Transformer NMT model provided by PyTorch Hub to translate a paragraph from English to French. This model is based on the 'Attention Is All You Need' paper and has been trained using large-scale semi-supervised training, back-translation, and noisy-channel reranking.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\"}###"}
{"prompt":"A CEO of a podcast company is trying to write summaries of the podcasts. She needs an API that can convert the main points of a podcast to text.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Tacotron 2 model from PyTorch Hub to convert the main points of a podcast to text.', 'code': 'import torch\ntacotron2 = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')'}###"}
{"prompt":"As a developer, I'm looking for an image classification API that has high accuracy.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'For a high accuracy image classification task, load the pretrained MEAL V2 model with ResNet-50 architecture from PyTorch Hub.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"Tell me how to classify an image based on different object categories with the highest performance. Give me an API to do that.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL-V2 (MEAL Version 2) model from PyTorch Hub for state-of-the-art image classification into different object categories.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"Recommend a highly-performing image classification model that does not rely on tricks such as architecture modification, data augmentation or label smoothing.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL-V2 model for image classification from PyTorch Hub. It achieves state-of-the-art results without using common tricks like architecture modification, data augmentation, or label smoothing.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I need an image classification API to predict the highest possible accuracy on ImageNet.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model based on ResNet50 and MobileNet V3 architectures to achieve a high top-1 accuracy on the ImageNet dataset.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"A developer in your team is working on an image classification app and would like to use an efficient pretrained model. Offer an API that can assist him.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub, which has a MobileNet V3-Large architecture, providing a balance between accuracy and efficiency for image classification tasks.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"Help me find an API to classify different dog breeds from their image. Our product integrates with Instagram.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3-Small 1.0 architecture for image classification, which can be fine-tuned to classify different dog breeds based on their images from Instagram.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\"}###"}
{"prompt":"Generate an API that classifies images with enhanced accuracy and without depending on architecture modification, data augmentation, or other training tricks.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 EfficientNet-B0 model from PyTorch Hub to classify images with high accuracy without using external training data, architecture modifications, or other tricks.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"Find an API that can perform scene recognition in videos and is pretrained on a large dataset for enhanced accuracy.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained 3D ResNet model (slow_r50 variant) from PyTorch Hub, which performs scene recognition in videos using the Kinetics 400 dataset.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"I need an API to detect objects in a traffic video, segment drivable areas, and detect lane lines. Provide an API that can perform this task.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HybridNets model from PyTorch Hub, which is an end-to-end perception neural network that can detect traffic objects, segment drivable areas, and perform lane detection.\", 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"I am designing an autonomous navigation system for a robot. What API should I use that detects objects, drivable areas, and lane lines simultaneously?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the YOLOP model from PyTorch Hub, which is an efficient multi-task network for object detection, drivable area segmentation, and lane detection in real-time.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"Is there any technique that can compute relative depth from a single image? Which API can do this job?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which can compute relative depth from a single image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"Help me extract the depth information from a single image taken with my camera. Provide an API that can do this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MiDaS DPT_Hybrid model from PyTorch Hub, which can compute relative depth information from a single image.\", 'code': \"import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\"}###"}
{"prompt":"I have a mix of pop songs and I need an API that can break it into separate tracks for vocals, drums, etc.\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': 'separator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Open-Unmix model from PyTorch Hub to separate pop music into vocals, drums, bass, and other instruments.', 'code': 'import torch\nseparator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)'}###"}
{"prompt":"Help me choose an API that can identify the specific species of the bird in a given photograph.\n\n###\n\n","completion":" {'domain': 'Fine-grained Classification', 'api_call': \"model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NTS-Net model for bird species identification from PyTorch Hub. The model is fine-tuned on the CUB200 2011 dataset, which consists of 200 bird species.', 'code': \"import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\"}###"}
{"prompt":"Create a high-resolution image segmentation model that can distinguish between objects in a scene.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the DeepLabV3 model with a ResNet-50 backbone pretrained on the COCO train2017 dataset to perform high-resolution semantic segmentation of input images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"What is an efficient method to transcribe voicemails into text using an API?\n\n###\n\n","completion":" {'domain': 'Speech-To-Text', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_stt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained Silero Speech-to-Text model from PyTorch Hub to efficiently transcribe voicemails into text.', 'code': 'import torch\nmodel, decoder, utils = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', language='en', device=torch.device('cpu'))'}###"}
{"prompt":"An edTech company wants to build an app that reads content from their textbook to the students. Suggest an API that can convert written text to speech.\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model = torch.hub.load('snakers4\/silero-models', 'silero_tts', language=language, speaker=speaker, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Silero Text-To-Speech model from PyTorch Hub, which can convert written text to naturally sounding speech for educators and students.', '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)\"}###"}
{"prompt":"Find me an API that can generate low-resolution images of clothing items.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Deep Convolutional Generative Adversarial Networks (DCGAN) model from PyTorch Hub, which is efficient for generating low-resolution images of clothing items.', 'code': 'import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)'}###"}
{"prompt":"A startup wants to build an application that helps users categorize the images they upload. Can you recommend an API for that?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ResNext WSL model pretrained on PyTorch Hub for high-accuracy image classification.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)'}###"}
{"prompt":"Provide me with an image classification API that uses weakly-supervised training and can achieve high accuracy on the ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub, which is trained using weakly-supervised learning on a large-scale dataset and achieves high accuracy on the ImageNet dataset.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"I am working on a computer vision task for my company, and I would like a well-performing machine learning API that can provide correct accuracy in classifying images of objects.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ResNext WSL model from PyTorch Hub with pre-trained weights for classification of objects in images. The model is trained on ImageNet, ensuring high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"I have a brain MRI scan and I want to detect abnormalities in the scan. Can you recommend an API for this task?\n\n###\n\n","completion":" {'domain': 'Medical Image Analysis', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained U-Net model for brain MRI segmentation in PyTorch Hub. This model can segment abnormalities in brain MRI scans with 3 input channels from pre-contrast, FLAIR, and post-contrast sequences.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"A prankster is building a fake celebrity face generator. Recommend an API that can generate realistic fake celebrity faces.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub, which is capable of generating realistic, high-quality fake celebrity faces.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', pretrained=True)'}###"}
{"prompt":"Abby wants to identify the objects present in her collection of images. Identify a model that can analyze Abby's images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NVIDIA ResNeXt101-32x4d model from PyTorch Hub to classify the objects in the images collected by Abby.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)'}###"}
{"prompt":"Imagine you received a huge dataset of unidentified animals, and you need to classify them according to their species. Provide me an API that can classify them given their image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained AlexNet model from PyTorch Hub for image classification tasks, which can be fine-tuned to classify animal species given their images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"How can I get an accurate text classification model based on BERT but with improvements?\n\n###\n\n","completion":" {'domain': 'Text Classification', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained RoBERTa model from PyTorch Hub, which is an improved version of BERT fine-tuned for better text classification.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/fairseq\", \"roberta.large\", pretrained=True)'}###"}
{"prompt":"I need an API to classify objects within images efficiently using mixed precision training for better performance.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101-32x4d model from PyTorch Hub, which uses mixed precision training and Tensor Cores on NVIDIA GPUs for efficient object classification within images.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d')\"}###"}
{"prompt":"Convert an English text to speech while preserving the naturalness of the voice. Recommend me a suitable API.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': 'waveglow = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_waveglow\", model_math=\"fp32\")', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained WaveGlow model from PyTorch Hub, which works in conjunction with the Tacotron 2 model, to synthesize natural-sounding speech from text input.', 'code': 'import torch\nwaveglow = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_waveglow\", model_math=\"fp32\")'}###"}
{"prompt":"I am a data scientist who needs to classify images using a neural network with a dense structure. Recommend an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-121 model from PyTorch Hub for dense structure image classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"I am building an image recognition app, and I need to classify objects in images. Which API should I use?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Densenet169 model from PyTorch Hub for image classification, which can recognize objects in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"I need an API suitable for classifying skin conditions based on images. What do you suggest?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-201 model from PyTorch Hub, which can be fine-tuned for skin condition classification tasks.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"densenet201\", pretrained=True)'}###"}
{"prompt":"Which API do I use for segmenting objects in an image based on their class?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the FCN-ResNet50 semantic segmentation API from PyTorch Hub, which is pretrained on the COCO dataset and can segment objects in an image based on their class.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)'}###"}
{"prompt":"I am developing an AI vacuum cleaner that can identify type of object on the floor so it can avoid it. I am looking for an API to help me in idenfifying common objects like shoes, toys, type of dirt. You may assume I already have pictures of the floor.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet from the PyTorch Hub for identifying common objects on the floor, such as shoes, toys, or types of dirt.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision:v0.10.0', 'densenet161', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I am working on environmental analysis and I want to analyze each pixel in an aerial image and identify its class. What API could help me with that?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained FCN-ResNet101 model for semantic segmentation from PyTorch Hub to analyze each pixel in the aerial image and classify it into corresponding categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)'}###"}
{"prompt":"I'm looking for a computationally efficient image classifier. Please suggest a suitable API for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Harmonic DenseNet (HarDNet) model from PyTorch Hub for computationally efficient image classification.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"I have a collection of images to classify. Can you suggest an API with good accuracy and less computational cost?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68 model from PyTorch Hub, which is efficient, fast and has low memory traffic, making it suitable for image classification tasks with good accuracy.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"I am trying to design an app that can classify shoes, clothes, and other fashion items from images taken by users. Recommend me an API that can perform this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'harDnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68DS model from PyTorch Hub, which can be fine-tuned for classification of fashion items such as shoes and clothes.', 'code': 'import torch\nmodel = torch.hub.load(\"PingoLH\/Pytorch-HarDNet\", \"harDnet68ds\", pretrained=True)'}###"}
{"prompt":"Recommend me an API to classify images of cats and dogs.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HarDNet-85 model from PyTorch Hub, which can be fine-tuned for cat and dog classification given an input image.\", 'code': 'import torch\nmodel = torch.hub.load(\u2018PingoLH\/Pytorch-HarDNet\u2019, \u2018hardnet85\u2019, pretrained=True)'}###"}
{"prompt":"Tell me an API that can classify photos of plants into their respective species.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GoogLeNet model from PyTorch Hub, which can be fine-tuned for plant species classification.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\"}###"}
{"prompt":"I need an API for classifying types of plants through an image. Write me an example.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model from PyTorch Hub, which can be fine-tuned for plant classification tasks given an input image.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"Help me find an API that can recognise appearances and domains in cross domain tasks like person or vehicle re-identification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model for ResNet-101-IBN-a from PyTorch Hub, which recognizes appearances and domains in cross-domain tasks such as person or vehicle re-identification.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"One of the largest retailers, Walmart, wants to develop a system for automating price audits to replace the manual way of checking prices. Suggest an API that can identify items in a picture and compare them with data from the server to achieve the retailer's goal.\n\n###\n\n","completion":" {'domain': 'Object Classification', 'api_call': 'model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GhostNet model from PyTorch Hub, which can be fine-tuned for object classification of items in a picture, enabling price audit automation at the retailer.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)'}###"}
{"prompt":"Identify an API that can deal with vehicle re-identification tasks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net (ResNeXt-101-IBN-a) model from PyTorch Hub for person\/vehicle re-identification tasks, which unifies instance normalization and batch normalization.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnext101_ibn_a', pretrained=True)'}###"}
{"prompt":"I need an AI-based image recognition technology to enhance a photo journal app. Please recommend an API.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Inception v3 model from PyTorch Hub, an AI-based image recognition technology suitable for enhancing photo journal apps.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\"}###"}
{"prompt":"I'm facing a classification problem that involves a high level of domain adaptation. Suggest me an API that suits my task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model from PyTorch Hub, which is designed with domain\/appearance invariance for cross-domain classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"Looking for an API that can classify thousands of different species of flowers from an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt50 model from PyTorch Hub, which can be fine-tuned for flower species classification from images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"Recommend an image classification API that can accurately predict items in a given image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNeSt-269 model from PyTorch Hub, an image classification model with high accuracy for predicting items in images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)'}###"}
{"prompt":"I need to know which API would be suitable to perform image classification on CPU optimized hardware.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the ProxylessNAS model optimized for CPU from PyTorch Hub for efficient image classification on CPU-based hardware.\", 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)'}###"}
{"prompt":"I am designing a wildlife preservation journal that needs to categorize pictures of different animal species. Suggest a machine learning API to classify the animal species in a given image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model from PyTorch Hub, which can be fine-tuned for animal species classification given an input image of an animal.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)'}###"}
{"prompt":"Create a mobile application that optimizes image classification. Recommend an API that I can use for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ProxylessNAS Mobile model from PyTorch Hub, which is optimized for mobile platforms and offers high accuracy for image classification.\", 'code': \"import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\"}###"}
{"prompt":"I require a lightweight API that can perform image classification on mobile devices.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load MobileNet v2 from PyTorch Hub, a lightweight model optimized for image classification tasks on mobile devices.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)'}###"}
{"prompt":"I need help selecting an API that can classify an image based on an optimized CNN architecture for GPU devices.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the ProxylessNAS model from PyTorch Hub, optimized for GPU devices, to classify images using state-of-the-art neural architecture search.', 'code': 'import torch\nmodel = torch.hub.load(\"mit-han-lab\/ProxylessNAS\", \"proxylessnas_gpu\", pretrained=True)'}###"}
{"prompt":"I need to execute an image classification API that can identify objects in a given image. Recommend an API and show me how to use it.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet18 model from PyTorch Hub for image classification tasks. The model can identify objects in a given image using its pre-trained weights.\", 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnet18\", pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Find me a pre-trained model for general image recognition that I can use for an e-commerce website's product categorization.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNet-34 model from PyTorch Hub for general image recognition, which can be used for e-commerce product categorization.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"I want to classify an image using a pre-trained deep learning model. Find me an API I could use.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet152 model from PyTorch Hub to perform image classification on a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"I am working on building a dog breed identification app, recommend an API that can be used for this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from PyTorch Hub, which can be fine-tuned for dog breed identification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)'}###"}
{"prompt":"I would like an API that can classify land animals from an image using the latest deep learning techniques.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext model from PyTorch Hub, which can be fine-tuned for classifying land animals from images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)'}###"}
{"prompt":"A wildlife photographer needs an API to help in sorting birds and mammals in his photo collection. Suggest an API.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext101 model from PyTorch Hub, which can be used for image classification and fine-tuned for sorting birds and mammals in the photo collection.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)'}###"}
{"prompt":"Identify a library API suitable for classifying images with high accuracy while keeping FLOPs low.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the SNNMLP Tiny model from PyTorch Hub, which provides high accuracy image classification with low FLOPs, making it efficient for your task.\", 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)'}###"}
{"prompt":"I want to classify an image with a neural network that has lower operation complexity. What's the best API to use?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SNNMLP Small model from PyTorch Hub, which provides high accuracy with lower FLOPs, making it ideal for image classification tasks with low operation complexity.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"Is there an API I can use for image categorization in a mobile app? I need it to be lightweight and fast.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SqueezeNet 1.1 model from PyTorch Hub, a lightweight and fast image classification model suitable for mobile applications.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)'}###"}
{"prompt":"I am an independent developer working on a chatbot. I need my chatbot to identify what is in the user's picture. Suggest an pre-trained API that can classify objects in an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ShuffleNet v2 model from PyTorch Hub, which can classify objects in images with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)'}###"}
{"prompt":"I need an image classification API for a robot that can recognize an object in real time with high accuracy.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP Base model from PyTorch Hub, which is a high accuracy image classification model suitable for real-time object recognition.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)'}###"}
{"prompt":"A retail analyst wants to process an image containing a collection of consumer products and classify the objects in the image. Find an API that helps categorize the objects in the image and provide a confidence score for each category., 'Input': '<noinput>'\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained SqueezeNet model, an image classification model with 50x fewer parameters than AlexNet, to classify the objects in the image and provide confidence scores for each category.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"squeezenet1_0\", pretrained=True)'}###"}
{"prompt":"I am building a photo app and I need an API that can classify images into categories. Do you have any suggestions?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pre-trained VGG13 model from PyTorch Hub for image recognition and classification into categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)'}###"}
{"prompt":"Develop a mobile app which can identify 1000 objects based on the camera, provide an API that can be used to develop it.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load(repo_or_dir='pytorch\/vision', model='vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model with batch normalization for image recognition from PyTorch Hub to identify 1000 different objects in a mobile app.', 'code': 'import torch\nmodel = torch.hub.load(repo_or_dir=\"pytorch\/vision\", model=\"vgg11_bn\", pretrained=True)\nmodel.eval()' }###"}
{"prompt":"A team is working on automatically tagging animals in pictures taken by wildlife cameras. Provide an API for a model that can perform this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from PyTorch Hub, which can be fine-tuned for wildlife animal classification given images from wildlife cameras.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"I want to recognize objects in images using a VGG13 model with batch normalization. What API should I use for this?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model with batch normalization from PyTorch Hub to perform object recognition in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)'}###"}
{"prompt":"Identify an API that can recognize objects inside photos. Ensure that it is pretrained and ready for use.\n\n###\n\n","completion":" {'domain': 'Object Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model with batch normalization from PyTorch Hub. This model is suitable for recognizing objects within images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"vgg16_bn\", pretrained=True)'}###"}
{"prompt":"Identify an API that can recognize what type of buildings are in an image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the VGG19 model from the PyTorch hub and fine-tune it to classify images of different types of buildings.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)'}###"}
{"prompt":"Can you recommend an API that can recognize objects in images, like different species of animals?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', model='vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG16 model from PyTorch Hub to recognize objects in images, such as different species of animals, using the ImageNet dataset's trained weights.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', model=\\'vgg16\\', pretrained=True)'}###"}
{"prompt":"An automotive website has different types of vehicles in a gallery; they want to automatically classify these vehicles into different categories based on an image. Tell me how to achieve this using an API.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model with batch normalization from PyTorch Hub, which can be fine-tuned for classifying vehicles into different categories based on images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"Generate code that will help me classify different dog breeds in a photo.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-101-2 model from PyTorch Hub, which can be fine-tuned for dog breed classification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"I need an API that can help me classify various objects in an image from a popular repository. Provide a pretrained model for the same.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-50-2 model from PyTorch Hub, which can be used for classifying various objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"As a content moderator, I need to classify videos into categories Automatically. Please suggest an API for performing this task.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained SlowFast model for video classification from PyTorch Hub to automatically classify video clips into different categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)'}###"}
{"prompt":"Tell me an API that can classify a video clip according to the action in the clip.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained X3D model from PyTorchVideo, which can classify video clips into different action categories based on the content of the clip.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"Is there an NLP API that can summarize long articles or documents?\n\n###\n\n","completion":" {'domain': 'NLP', 'api_call': \"model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\", 'api_provider': 'Hugging Face', 'explanation': 'Load the pretrained BERT model from Hugging Face PyTorch-Transformers, which can be fine-tuned for text summarization tasks on long articles or documents.', 'code': \"import torch\nmodel = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\"}###"}
{"prompt":"Suggest a machine learning API that can be used to analyze images of protein structures to predict their cellular localization.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet model from the Facebook Research Semi-supervised and Semi-weakly Supervised ImageNet Models collection. Fine-tune the model to classify protein structure images', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"I\u2019m working on a mobile app, and I need an efficient neural network for image classification. What API should I use?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the Once-for-All (OFA) Networks, which are specifically designed for mobile and efficient image classification.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"The Amazon security team wants to auto-detect unattended package left at their headquarters. Propose an API that can detect objects in images.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SSD (Single Shot MultiBox Detector) model from PyTorch Hub, which can be used to detect objects, including unattended packages, in images.\", 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"What machine learning API should I use to convert a text to speech?\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Use the Tacotron 2 model available through PyTorch Hub, which generates natural-sounding speech from input text without any additional prosody information.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')'}###"}
{"prompt":"I need an efficient image classification API that can be used to recognize complex objects in images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with ResNet50 architecture from PyTorch Hub, which is capable of recognizing complex objects in images with high accuracy.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"The Vegan Society is doing research on vegan products by automatically labeling Instagram images depicting different types of food. Provide an API that can identify and classify objects in an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V1', 'mealv1_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model based on ResNet50 from PyTorch Hub to perform image classification and identify objects in an image.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V1', 'mealv1_resnest50', pretrained=True)\"}###"}
{"prompt":"Recommend an API to enhance classification accuracy for a popular machine learning model for tagging animals in images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with ResNet50 backbone and CutMix augmentation from PyTorch Hub to achieve enhanced classification accuracy for tagging animals in images.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"Design a machine learning API to identify the type of objects present in an image with high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub, which is designed to classify objects in images with high accuracy without relying on additional tricks.', 'code':\"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"Create a tool to predict what would be in an image taken outdoors in the wild, and recommend an API that could accomplish this.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub with a ResNet50 backbone, which can predict the contents in outdoor wild images accurately.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\"}###"}
{"prompt":"Recommend an API that can identify different breeds of dogs from photographs.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL_V2 EfficientNet-B0 model from PyTorch Hub, which can be fine-tuned for identifying different dog breeds from photographs.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"Recommend an API to classify images with a high level of accuracy without using any tricks.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"torch.hub.load(repo_or_dir='szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the MEAL V2 model from PyTorch Hub, which achieves state-of-the-art performance on image classification without relying on any tricks or modifications.', 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_mobilenetv3_small_100', pretrained=True)'}###"}
{"prompt":"I'm an app developer and I need an API to classify which type of animal is in a particular image. What do you suggest?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model for image classification from PyTorch Hub. This model is optimized for high accuracy and performance and can classify various objects, including animals.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"Suggest a pre-trained video classification model capable of classifying human actions in a video.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained 3D ResNet model 'slow_r50' from PyTorch Hub, which can classify human actions in a video using its pre-trained knowledge from the Kinetics-400 dataset.\", 'code': 'import torch\nmodel = torch.hub.load(\\'facebookresearch\/pytorchvideo\\', \\'slow_r50\\', pretrained=True)'}###"}
{"prompt":"I need an API capable of detecting vehicles, drivable areas, and lane lines from images taken from a dashboard camera.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HybridNets model from PyTorch Hub, which is specialized for traffic object detection, drivable area segmentation, and lane detection from images taken by a dashboard camera.\", 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"Help me to find an API to assist autonomous vehicles providing object detection, drivable area segmentation and lane detection for safer navigation.\n\n###\n\n","completion":" {'domain': 'Autonomous Vehicle Assistance', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub for object detection, drivable area segmentation, and lane detection to assist autonomous vehicles in safe navigation.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"I want an API that can deliver relative depth information from a single photo.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which can compute relative depth from a single image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"Suppose I have to design an autonomous car which uses a depth perception system. Suggest me an API which can compute relative depth from a single image.\n\n###\n\n","completion":" {'domain': 'Depth Perception', 'api_call': \"model = torch.hub.load(repo_or_dir='intel-isl\/MiDaS', model='DPT_Large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model from PyTorch Hub, which calculates relative depth from a single image for depth perception systems.', 'code': 'import torch\nmodel = torch.hub.load(repo_or_dir='intel-isl\/MiDaS', model='DPT_Large', pretrained=True)'}###"}
{"prompt":"I have a single image and want to compute its depth map. Suggest me an API for this task.\n\n###\n\n","completion":" {'domain': 'Depth Estimation', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS DPT_Hybrid model from PyTorch Hub, which can compute relative depth maps from a single input image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)'}###"}
{"prompt":"How can I extract the vocal track and other stems from a song's audio? Suggest an API for this.\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"model = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Open-Unmix model for music source separation from PyTorch Hub, which can separate a song into vocal, drums, bass and other instruments stems.', 'code': \"import torch\nseparator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\"}###"}
{"prompt":"Can you find me an API that identifies a species of bird from an image?\n\n###\n\n","completion":" {'domain': 'Fine-grained Image Classification', 'api_call': 'model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, topN=6, device='cpu', num_classes=200)', 'api_provider': 'PyTorch', 'explanation': 'Load the NTS-Net model from PyTorch Hub, which is pretrained on the CUB200 2011 dataset to perform fine-grained classification of bird species.', 'code': 'import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, topN=6, device='cpu', num_classes=200)'}###"}
{"prompt":"I have a voice assistant project and I want to convert the user's speech to text. Can you recommend an API suitable for this?\n\n###\n\n","completion":" {'domain': 'Speech-To-Text', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_stt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Speech-To-Text models from PyTorch Hub, which are robust and can handle multiple languages.', 'code': 'import torch\nmodel, decoder, utils = torch.hub.load(repo_or_dir=\"snakers4\/silero-models\", model=\"silero_stt\", language=\"en\", device=torch.device(\"cpu\"))'}###"}
{"prompt":"Recommend me an API for detecting the presence of speech in a given audio file.\n\n###\n\n","completion":" {'domain': 'Voice Activity Detection', 'api_call': \"model = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero VAD, a pre-trained enterprise-grade Voice Activity Detector (VAD) from PyTorch Hub, to detect the presence of speech in an audio file.', 'code': 'import torch\nmodel, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)'}###"}
{"prompt":"I need to extract objects from a picture of a city scene. Provide me with an API that can output the segmented objects from an image.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with ResNet-101 backbone from PyTorch Hub to segment objects from an input city scene image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\nmodel.eval()' }###"}
{"prompt":"Develop an API that detects objects in an image given the URL of the image. The API should work with high accuracy.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5 model from PyTorch Hub to detect objects in an image provided by the URL, offering high accuracy for object detection.', 'code': 'import torch\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)'}###"}
{"prompt":"Find an API to correctly classify my travel photos based on the displayed location.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model for image classification from PyTorch Hub to classify travel photos based on locations.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Develop a model to generate new clothing design samples using GAN (Generative Adversarial Networks).\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': 'Load the DCGAN model from PyTorch Hub with pretrained weights on the FashionGen dataset for generating new clothing designs.', 'code': \"import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\"}###"}
{"prompt":"I run an online marketplace where users want to categorize their items based on images. What API should they use for this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the ResNext WSL model for image classification from PyTorch Hub to categorize items based on images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"Explain how to use an API for semantic segmentation of park scenes to identify different regions such as playgrounds, pavements and water.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with a MobileNet_V3 backbone for semantic segmentation.', 'code': 'import torch\nfrom PIL import Image\nfrom torchvision import transforms\n\nmodel = torch.hub.load(\"pytorch\/vision\", \"deeplabv3_mobilenet_v3_large\", pretrained=True)\nmodel.eval()\n\ninput_image = Image.open(\"park_scene.jpg\")\ninput_image = input_image.convert(\"RGB\")\n\npreprocess = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n\ninput_tensor = preprocess(input_image)\ninput_batch = input_tensor.unsqueeze(0)\ninput_batch = input_batch.to(\"cuda\") if torch.cuda.is_available() else input_batch\nmodel.to(\"cuda\") if torch.cuda.is_available() else model\n\nwith torch.no_grad():\n    output = model(input_batch)[\"out\"][0]\noutput_predictions = output.argmax(0)'}###"}
{"prompt":"Propose an API that can recognize objects in an image with high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt WSL model from PyTorch Hub, which is capable of accurately recognizing objects in images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl', pretrained=True)'}###"}
{"prompt":"Could you suggest a GAN model to generate high-quality human faces images?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub, which can generate high-quality human face images.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', pretrained=True)'}###"}
{"prompt":"How can I use machine learning to identify different land use types from an aerial photograph or satellite imagery?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained AlexNet model from PyTorch Hub, which can be fine-tuned for land use classification based on aerial photographs or satellite images.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\"}###"}
{"prompt":"A photographer wants to classify the objects in his images. Provide an API that can be used for image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from PyTorch Hub for image classification tasks, which can classify objects in images with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_resnet50\", pretrained=True)'}###"}
{"prompt":"Help the e-commerce company in developing an API for identifying and classifying images of products.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101-32x4d model from the NVIDIA Deep Learning Examples repository, and utilize it for image classification of e-commerce products.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d')\"}###"}
{"prompt":"I want a model that can classify text into positive, negative or neutral sentiment.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained RoBERTa model from PyTorch Hub, then fine-tune it for sentiment classification, including positive, negative, and neutral categories.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)'}###"}
{"prompt":"\"A Language teacher is teaching his class about pronunciation of English. They are looking for a TTS API that can help the students in the study process. Suggest an API for this purpose.\"\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained WaveGlow model for text-to-speech, combined with Tacotron 2, to provide natural-sounding speech synthesis, helping students learn pronunciation.', 'code': 'import torch\nwaveglow = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_waveglow\", pretrained=True)'}###"}
{"prompt":"A marketing team needs to classify images for their advertisement targeting. Can you offer an API that can predict the class of an image?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use a pre-trained ResNeXt101-32x4d model from NVIDIA for image classification. The model is trained on the ImageNet dataset and can predict object classes in images.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)\"}###"}
{"prompt":"I require a deep learning model to perform image classification on a dataset with multiple classes. Recommend an API that serves this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-201 model from PyTorch Hub to perform image classification on a dataset with multiple classes.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"Inform me about an API that can aid in image classification tasks and has a dense network architecture.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DenseNet-161 model from PyTorch Hub, which has a dense network architecture and can be used for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet161', pretrained=True)'}###"}
{"prompt":"An urban planning team needs to segment a satellite image to identify different landforms. Can you suggest an ML API that can provide segmentation maps?\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet101 model from PyTorch Hub, which can be fine-tuned for semantic segmentation of satellite images to identify different landforms.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)\"}###"}
{"prompt":"Propose an API to process a satellite image and label different objects like buildings, roads, and vegetation.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet50 model from PyTorch Hub, a Fully-Convolutional Network designed for semantic segmentation, to process satellite images and label different objects such as buildings, roads, and vegetation.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Find an API that can optimize a model for low memory usage and fast processing to differentiate between cats and dogs in images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HarDNet model from PyTorch Hub, an efficient and low-memory model that can be fine-tuned for differentiating between cats and dogs in images.\", 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"Suggest an API that classifies images quickly, without sacrificing too much accuracy, as compared to traditional methods like ResNet.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-85 model from PyTorch Hub, which is faster and more efficient than traditional methods like ResNet, without sacrificing much accuracy.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)'}###"}
{"prompt":"A machine learning engineer is working on a project to classify images of different dog breeds. Provide an example of an API that could be used for image classification.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68 model from PyTorch Hub, which is efficient for image classification and can be fine-tuned for classifying dog breeds.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"Recommend me a computer vision model that can determine the top 5 labels for an image.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GoogLeNet model from PyTorch Hub, which can be used to classify images by returning the top 5 labels.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"googlenet\", pretrained=True)'}###"}
{"prompt":"Present me an API that efficiently classifies images with less computational cost compared to traditional methods.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GhostNet model from PyTorch Hub for image classification. GhostNet is an efficient network architecture that generates more features using cheaper operations, offering superior speed and accuracy tradeoff.\", 'code': 'import torch\nmodel = torch.hub.load(\\'huawei-noah\/ghostnet\\', \\'ghostnet_1x\\', pretrained=True)'}###"}
{"prompt":"We need to detect objects in images for a mobile platform with a low memory footprint CNN architecture. Could you suggest an appropriate API for this purpose?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-68DS model from PyTorch Hub, which is designed for efficient object detection in images with low memory and computational requirements.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)'}###"}
{"prompt":"Recommend an API for a vehicle recognition system which can identify models and types of vehicles from various visual angles.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-101-IBN-a model from PyTorch Hub, which can be fine-tuned for vehicle recognition and classification.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"A live streaming platform needs a machine learning model for content moderation. Tell me how to implement an API that can classify an image for content moderation.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a model from PyTorch Hub, which can be fine-tuned for content moderation by classifying an image into appropriate categories.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"We have a dataset concerning pedestrian activities at crosswalks. Recommend an API capable of predicting activities like walking, running, or waiting given an image as input.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(\"XingangPan\/IBN-Net\", \"resnext101_ibn_a\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt-101-IBN-a model from PyTorch Hub and fine-tune it for activity classification, such as walking, running, or waiting from images.', 'code': 'import torch\nmodel = torch.hub.load(\"XingangPan\/IBN-Net\", \"resnext101_ibn_a\", pretrained=True)'}###"}
{"prompt":"AML, a cybersecurity company is building a face identification engine for airports. Recommend an API that can output True if the two photos provided are the same person or False if not.\n\n###\n\n","completion":" {'domain': 'Face Verification', 'api_call': 'model = torch.hub.load('nyoki-mtl\/arcface', 'iresnet18', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Arcface model from PyTorch Hub that can be used to compute the embeddings for given photos and compare the similarity between the embeddings to determine if the two photos are of the same person.', 'code': 'import torch\nmodel = torch.hub.load('nyoki-mtl\/arcface', 'iresnet18', pretrained=True)'}###"}
{"prompt":"We want to design an app to identify plants. Recommend an API that can be used to classify images of plants.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Inception v3 model from PyTorch Hub and fine-tune it for plant image classification.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)'}###"}
{"prompt":"I am working at Pinterest and we want to build a classifier for the images in the platform. Give me an API that can classify images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model for image classification from PyTorch Hub to classify various types of images for the Pinterest platform.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest50', pretrained=True)'}###"}
{"prompt":"I want to create an app to recognize plants based on images. Suggest me an API that I can use for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-200 model from PyTorch Hub, which can be fine-tuned for plant recognition from images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest200', pretrained=True)'}###"}
{"prompt":"A museum located in New York City wants to categorize their art collection by automatically identifying the content of the artwork. Which API can be used to perform image classification?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model for image classification from PyTorch Hub to automatically identify and categorize the content of artworks.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"Let me know an API that can classify food from photos. It should be designed to run on low-power devices and offer good accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS model from PyTorch Hub, which is optimized for low-power devices (CPU) and can be fine-tuned for food classification.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)'}###"}
{"prompt":"I'm doing an image classification project, suggest an API that can help me classify images effectively.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-269 model from PyTorch Hub for effective image classification. ResNeSt models are ResNet variants that utilize Split-Attention Networks, providing improved performance in classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)'}###"}
{"prompt":"Identify an API suitable for classifying images using a neural network optimized for high performance on GPUs.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the ProxylessNAS model optimized for GPUs from PyTorch Hub with pre-trained weights for image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"The Clubhouse App wants a function for image classification. Advise them an API that could be a potential solution for them.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model for image classification from PyTorch Hub to classify images into various classes, suitable for the Clubhouse App.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"mobilenet_v2\", pretrained=True)'}###"}
{"prompt":"I am from Google and I am looking for an API that can classify images into categories using neural networks optimized for mobiles.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ProxylessNAS Mobile model from PyTorch Hub, which is optimized for mobile platforms and can perform image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"Recommend an API to determine the type of objects in an image given a set of pre-trained labels.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet34 model from PyTorch Hub, which can be used for image classification tasks to recognize various objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)'}###"}
{"prompt":"Recommend to me an API that can efficiently recognize faces from an image and classify them into particular groups or categories.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet18 model from PyTorch Hub, which can be fine-tuned for face recognition and classification into specific groups or categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)'}###"}
{"prompt":"Recommend me a model that has a high accuracy in recognizing the emotion of people displayed in images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50 model from PyTorch Hub, which can be fine-tuned for emotion recognition in facial images with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)'}###"}
{"prompt":"Looking for an API that can identify different breeds of dogs in images. It must provide high accuracy in identifying the breed.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load(\\\"pytorch\/vision\\\", \\\"resnet101\\\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the ResNet101 model from PyTorch Hub. The model is pretrained on ImageNet and can be fine-tuned to classify dog breeds with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnet101\", pretrained=True)'}###"}
{"prompt":"I noticed that some of the bird species are mistakenly tagged in the bird photo collection. Provide me an API to auto fix the tags.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet152 model from PyTorch Hub, which can be fine-tuned for bird species identification and classification to automatically fix tags.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"How can I build an AI system that can recognize dogs and cats? Provide me an API and pretrained model that will help.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load a pre-trained ResNext model from the PyTorch Hub for image classification. It can be fine-tuned to distinguish between dogs and cats.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext50_32x4d', pretrained=True)'}###"}
{"prompt":"Analyze a given image for its content and classify it into its respective category. Suggest an API that can help with this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext model from PyTorch Hub for image classification. The model will process a given image and classify it into the respective category.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext101_32x4d', pretrained=True)'}###"}
{"prompt":"Find me an API that can classify images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP model from PyTorch Hub for image classification tasks, it achieves high accuracy with low computational cost.', 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\"}###"}
{"prompt":"I need to classify a set of random images. What pre-trained machine learning API would you recommend for this purpose?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the SNNMLP Tiny model from PyTorch Hub, which is optimized for image classification with high accuracy and low FLOPs, making it suitable for classifying random images.\", 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\"}###"}
{"prompt":"A company is looking to build an app that automatically scans items in the grocery store and categorizes them in real time. What API should they use?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained SqueezeNet model from PyTorch Hub for efficient image classification, as it achieves high accuracy with fewer parameters, making it ideal for real-time classification in a grocery store app.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)'}###"}
{"prompt":"Our company, New York Pillow, is developing an inventory system for our products, and we need a machine learning model to classify images of pillows by their type. Can you give me an API which can classify pillow types?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP Small model from PyTorch Hub, which can be fine-tuned for pillow classification.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"I need an API that can identify dog breeds in a given photo.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from the PyTorch Hub, which can be fine-tuned for dog breed identification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"Find an API that can classify a given image, while maintaining a high efficiency for mobile phone usage.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ShuffleNet v2 model from PyTorch Hub, which is designed for efficient image classification on mobile devices.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)'}###"}
{"prompt":"I'm working on building an app that would let users know what's in a photograph. Suggest an efficient image recognition API appropriate for my purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model with batch normalization (vgg11_bn) from PyTorch Hub for efficient image recognition tasks.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)\"}###"}
{"prompt":"Propose an API that can identify the type of car in a photo taken at a car dealership.\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG16 model from PyTorch Hub, which can be fine-tuned to identify the type of car in a photo taken at a car dealership.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"I am running a website on animal rescue and I have lots of animal photos during adoption process. Recommend me an API that can recognize animal breeds from images, so I can improve my website's user experience.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG-13 model from PyTorch Hub for image recognition tasks, and fine-tune it for recognizing animal breeds from images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)'}###"}
{"prompt":"What pre-trained API can I use to recognize different objects from an image?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Use the pre-trained VGG16 model with batch normalization from PyTorch Vision to recognize various objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)'}###"}
{"prompt":"A company is building an AI-powered image categorization tool to help label images in their database with greater accuracy. Recommend an API to identify and classify images with high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 with batch normalization (vgg13_bn) model from PyTorch Hub, which can accurately identify and classify images for the image categorization tool.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)'}###"}
{"prompt":"I run a pet adoption center and want to identify dog breeds for our customers. How can I use a pre-trained model to recognize dog breeds from images?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained VGG19-bn model from PyTorch Hub, and fine-tune it to recognize dog breeds from images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"A client of mine wants to classify photographs into relevant categories for their website. Can you recommend a suitable API to do the job?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide Residual Network 50 (Wide ResNet-50) from PyTorch Hub for robust image classification and organization.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)'}###"}
{"prompt":"A fitness app is trying to categorize workout videos based on the activity. Can you propose an API that can do this?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': \"Load the pretrained SlowFast model from PyTorchVideo to classify workout videos into different action categories based on the activity.\", 'code': \"import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\"}###"}
{"prompt":"I am building an app that can detect which animal is in a given image. Give me an API for image classification that would be suitable for this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-101 model from PyTorch Hub, which can be fine-tuned for animal detection and classification in a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"Recommend a video classification API that can identify the netball activity from a short video clip.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': 'Use the pretrained X3D model from the PyTorchVideo library, which is capable of classifying video clips into different action categories, including netball activity.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)'}###"}
{"prompt":"Recommend an API for classifying images that is efficient on edge devices.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Once-for-all (OFA) network with OFA_MobileNetV3 model for efficient image classification on edge devices.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"I am developing an app that would automatically tag uploaded images. Suggest an API that can perform image classification efficiently on a GPU.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained GPUNet model from NVIDIA, which is optimized for GPU performance and can classify images into different categories.', 'code': 'import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)'}###"}
{"prompt":"I need a pre-trained model to translate English text to French, which API can help perform that task?\n\n###\n\n","completion":" {'domain': 'Neural Machine Translation', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': \"Load the Transformer model from PyTorch Hub for English to French translation, using the WMT'14 dataset.\", 'code': \"import torch\nen2fr = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\"}###"}
{"prompt":"I need to develop an image classifier for an app that can recognize pictures of objects. Any recommended API that can do this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-18 model from Facebook Research Semi-supervised ImageNet Models library to classify images into different categories.', 'code': 'import torch\nmodel = torch.hub.load(\"facebookresearch\/semi-supervised-ImageNet1K-models\", \"resnet18_swsl\", pretrained=True)\nmodel.eval()'}###"}
{"prompt":"Can you recommend an NLP API for an AI chatbot that understands user's message and generates human-like responses?\n\n###\n\n","completion":" {'domain': 'Natural Language Processing', 'api_call': \"model = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')\", 'api_provider': 'Hugging Face\/PyTorch', 'explanation': 'You can use the \"BERT\" model from the \"PyTorch-Transformers\" library by Hugging Face to better understand user messages and generate human-like responses in your AI chatbot.', 'code': 'import torch\ntokenizer = torch.hub.load('huggingface\/pytorch-transformers', 'tokenizer', 'bert-base-cased')\nmodel = torch.hub.load('huggingface\/pytorch-transformers', 'model', 'bert-base-cased')'}###"}
{"prompt":"Develop a model to automatically identify plant species in images.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet-B0 model from PyTorch Hub, which can be fine-tuned for plant species identification from images.', 'code': \"import torch\nefficientnet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"Recommend a PyTorch API for converting text to speech with high-quality audio output.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Tacotron 2 model from NVIDIA, which is capable of converting text to high-quality speech using an encoder-decoder architecture.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math=\"fp16\")'}###"}
{"prompt":"I am building a security system for a shopping mall, and I need to identify objects in real-time from CCTV footage. Can you suggest an API?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pre-trained Single Shot MultiBox Detector (SSD) model from PyTorch Hub, which is highly efficient and accurate for real-time object detection in images.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"I need an API capable of classifying images with state-of-the-art accuracy using no tricks, for a call center to use.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with ResNet50, which achieves state-of-the-art image classification accuracy without using any tricks or modifications.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"What API can classify different types of dogs from an image?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model from PyTorch Hub, which can be used for dog breed classification from an image.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I want to classify common objects in an image. Recommend an API that can do this for me.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_mobilenetv3_small_075', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model with MobileNet V3 Small architecture from PyTorch Hub, which can classify common objects in an image.', 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_mobilenetv3_small_075', pretrained=True)'}###"}
{"prompt":"ABC fashion company wishes to use an API to classify the images of clothing styles. Suggest an API for this purpose.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model for image classification from PyTorch Hub. This model can be used to classify images of different clothing styles.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"Rankz wants to develop a custom video suggestion feature by classifying user's uploaded videos. Recommend a suitable API for this task.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained 3D ResNet model for video classification from PyTorch Hub to classify user-uploaded video clips into different categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'slow_r50', pretrained=True)'}###"}
{"prompt":"Can you tell me an API for classifying the content of an image?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'torch.hub.load(repo_or_dir=\"szq0214\/MEAL-V2\", \"meal_v2\", model=\"mealv2_efficientnet_b0\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'MEAL-V2 image classification API allows you to classify the content of an image using the pretrained EfficientNet-B0 model, which achieves high accuracy on ImageNet dataset.', 'code': 'import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model=\"mealv2_efficientnet_b0\", pretrained=True)'}###"}
{"prompt":"Present an API that boosts vanilla ResNet-50 to 80%+ top-1 accuracy on ImageNet for a farmer's crop identification project.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL-V2 model, which boosts the accuracy of ResNet-50, from PyTorch Hub for crop identification.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"I want to build a self-driving car software that can detect traffic objects, drivable areas, and lanes. Which API can perform these tasks efficiently?\n\n###\n\n","completion":" {'domain': 'Object Detection and Scene Understanding', 'api_call': \"model = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained HybridNets model from PyTorch Hub for efficient traffic object detection, drivable area segmentation, and lane detection in the context of self-driving cars.\", 'code': \"import torch\nmodel = torch.hub.load('datvuthanh\/hybridnets', 'hybridnets', pretrained=True)\"}###"}
{"prompt":"Create a multi-task network for autonomous driving to handle object detection, drivable area segmentation, and lane detection in real-time. Which API would you suggest to achieve this?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOP model from PyTorch Hub, an efficient multi-task network that can simultaneously handle object detection, drivable area segmentation, and lane detection for autonomous driving.', 'code': \"import torch\nmodel = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\"}###"}
{"prompt":"Design an intelligent robot. Tell me how to build a robot that can estimate objects' distance from its camera just by taking a photo.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained MiDaS model to estimate the relative depth of objects from a single image captured by the robot\\'s camera.', 'code': 'import torch\nmodel = torch.hub.load(\"intel-isl\/MiDaS\", \"DPT_Hybrid\", pretrained=True)'}###"}
{"prompt":"I am an architect for a construction project and I need to compute the depth of objects in an image. What API will allow me to do this?\n\n###\n\n","completion":" {'domain': 'Image Depth Estimation', 'api_call': 'midas = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large')', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MiDaS model (DPT_Large variant) from PyTorch Hub to compute the relative depth of objects in an image.', 'code': 'import torch\nmidas = torch.hub.load('intel-isl\/MiDaS', 'DPT_Large', pretrained=True)'}###"}
{"prompt":"I need a deep learning model to separate vocals from background music for a given audio clip. What API can I use for this?\n\n###\n\n","completion":" {'domain': 'Audio Separation', 'api_call': \"model = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained Open-Unmix model to separate the audio clip into vocals and background music using PyTorch.', 'code': 'import torch\nseparator = torch.hub.load('sigsep\/open-unmix-pytorch', 'umxhq')'}###"}
{"prompt":"I am a photographer and I want to determine the relative depth of objects in my images. Suggest an API that can compute the depth information from a single image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the MiDaS model from PyTorch Hub, which is designed to compute relative depth information from a single image.', 'code': 'import torch\nmodel = torch.hub.load('intel-isl\/MiDaS', 'MiDaS_small', pretrained=True)'}###"}
{"prompt":"An AI company wants to build a bird species identifier given an image of bird. Give me an API that can classify bird species from an input image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained NTS-Net model from PyTorch Hub, which is a fine-grained image classifier for bird species trained on the CUB200 2011 dataset.', 'code': \"import torch\nmodel = torch.hub.load('nicolalandro\/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})\"}###"}
{"prompt":"I'm working on a project that involves transcribing voice recordings. Recommend an API that can convert speech to text.\n\n###\n\n","completion":" {'domain': 'Speech-To-Text', 'api_call': 'model, decoder, utils = torch.hub.load(repo_or_dir=\\'snakers4\/silero-models\\', model=\\'silero_stt\\', language=\\'en\\', device=device)', 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Speech-To-Text (STT) model from PyTorch Hub for the English language. It will transcribe voice recordings into text.', 'code': 'import torch\ndevice = torch.device('cpu')\nmodel, decoder, utils = torch.hub.load(repo_or_dir=\\'snakers4\/silero-models\\', model=\\'silero_stt\\', language=\\'en\\', device=device)'}###"}
{"prompt":"Based upon a traffic camera, San Francisco Traffic Authority wants to identify the segmented boundaries for everything the camera captures. Provide an API that can help them perform this task.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with ResNet-50 backbone for performing semantic segmentation on the image captured by the traffic camera.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet50', pretrained=True)'}###"}
{"prompt":"Recommend an API that can detect if there is a speech or silence segments in an audio file.\n\n###\n\n","completion":" {'domain': 'Voice Activity Detection', 'api_call': \"model, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the Silero Voice Activity Detector from PyTorch Hub to detect speech and silence segments in audio files.\", 'code': \"import torch\nmodel, utils = torch.hub.load(repo_or_dir='snakers4\/silero-vad', model='silero_vad', force_reload=True)\"}###"}
{"prompt":"Tell me about an API that is capable of segmenting objects in images based on their semantics, like identifying a car or a person.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the DeepLabV3 ResNet-101 model from PyTorch Hub. This model is pretrained for semantic segmentation and can identify various objects in images, such as cars and people.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_resnet101', pretrained=True)'}###"}
{"prompt":"Can you recommend an API that can detect different types of animals in a picture?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained YOLOv5 model from PyTorch Hub, which is capable of detecting various types of animals in an image.', 'code': 'import torch\nmodel = torch.hub.load(\"ultralytics\/yolov5\", \"yolov5s\", pretrained=True)'}###"}
{"prompt":"I want to perform semantic segmentation on an image to differentiate the foreground objects from the background. Recommend an API that can accomplish this.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained DeepLabV3 model with MobileNet-V3 backbone for semantic segmentation from PyTorch Hub to differentiate foreground objects from the background in an image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'deeplabv3_mobilenet_v3_large', pretrained=True)'}###"}
{"prompt":"Help me to convert this text to speech: \"The weather is great today\" with an API call., 'Input': 'The weather is great today'\n\n###\n\n","completion":" {'domain': 'Text-To-Speech', 'api_call': \"model, symbols, sample_rate, example_text, apply_tts = torch.hub.load(repo_or_dir='snakers4\/silero-models', model='silero_tts', language='en', speaker='lj_16khz')\", 'api_provider': 'PyTorch', 'explanation': 'Load the Silero Text-To-Speech model from PyTorch Hub to convert the text \"The weather is great today\" into speech.', 'code': 'import torch\nlanguage = \"en\"\nspeaker = \"lj_16khz\"\nexample_text = \"The weather is great today\"\ndevice = torch.device(\"cpu\")\nmodel, symbols, sample_rate, _, 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)'}###"}
{"prompt":"I want to make a video game and I need randomly generated low resolution images for background. Can you suggest an API that can create images as per my requirement?\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Deep Convolutional Generative Adversarial Networks (DCGAN) model from PyTorch Hub. This model can generate low-resolution images suitable for your video game's background.\", 'code': \"import torch\nuse_gpu = True if torch.cuda.is_available() else False\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\"}###"}
{"prompt":"I need an API to classify an image according to the ImageNet dataset. Tell me which API to use and how.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pretrained ResNext WSL model from PyTorch Hub for image classification according to the ImageNet dataset.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x8d_wsl', pretrained=True)'}###"}
{"prompt":"Some friends and I want to organize a competition where people send us images of their pets and the photos are sorted into categories based on the type of pet in the image. Provide a deep learning API that can meet this goal.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext-101 32x16d WSL model from PyTorch Hub, which can be fine-tuned for classifying pet images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x16d_wsl', pretrained=True)'}###"}
{"prompt":"Help me discover an API suitable for classifying images in a custom dataset using Instagram users' preferences.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext WSL model from PyTorch Hub, which is trained on 940 million public images with 1.5K hashtags. This model can be fine-tuned for classifying images based on Instagram users\\' preferences.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x32d_wsl', pretrained=True)'}###"}
{"prompt":"A marketing company needs to clasify images of different catagories for a client. Can you provide an API to achive this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl')\", 'api_provider': 'PyTorch', 'explanation': 'Load the pre-trained ResNext WSL model from PyTorch Hub for image classification.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/WSL-Images', 'resnext101_32x48d_wsl')'}###"}
{"prompt":"We are building a comic generator software that creates new comic images by mixing scenes of other comics. Suggest a relevant API for generating such new comic images.\n\n###\n\n","completion":" {'domain': 'Image Generation', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Progressive Growing of GANs (PGAN) model from PyTorch Hub, which can be fine-tuned for generating new comic images by mixing scenes from other comics.', 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/pytorch_GAN_zoo:hub', 'PGAN', pretrained=True)'}###"}
{"prompt":"Teach me how to operate an API that can segment abnormalities in brain MRI images.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': \"model = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Use the pre-trained U-Net model and load it from PyTorch Hub for abnormality segmentation in brain MRI images.', 'code': 'import torch\nmodel = torch.hub.load('mateuszbuda\/brain-segmentation-pytorch', 'unet', in_channels=3, out_channels=1, init_features=32, pretrained=True)'}###"}
{"prompt":"A company is looking for an image recognition API to help them categorize their products. Suggest an API that could do this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 v1.5 model from PyTorch Hub to help categorize products using image recognition.', 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)\"}###"}
{"prompt":"Share an image classification API that can recognize objects in real-world images, including 1,000 categories.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeXt101-32x4d model from PyTorch Hub, capable of recognizing objects in real-world images across 1,000 categories.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_resneXt', pretrained=True)'}###"}
{"prompt":"A Facebook user wants to classify images from their vacation album. Recommend an AI-based solution that can classify images into different categories.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained SE-ResNeXt101 model, which is an image classification model that can classify vacation images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d', pretrained=True)'}###"}
{"prompt":"I need a suggestion for an API that can classify images of birds and flowers.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Densenet-121 model for image classification from PyTorch Hub to classify images of birds and flowers.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet121', pretrained=True)'}###"}
{"prompt":"Identify the breed of a dog in a given image using an API.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained AlexNet model from PyTorch Hub, which can be fine-tuned for dog breed classification given a dog's image.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'alexnet', pretrained=True)\"}###"}
{"prompt":"Recommend me an API that can create synthesized speech from text input.\n\n###\n\n","completion":" {'domain': 'Text-to-Speech', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_waveglow', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained WaveGlow model from NVIDIA PyTorch Hub, which can be used in combination with the Tacotron 2 model to synthesize natural-sounding speech from text input.', 'code': 'import torch\nmodel = torch.hub.load(\"NVIDIA\/DeepLearningExamples:torchhub\", \"nvidia_waveglow\", pretrained=True)'}###"}
{"prompt":"'Suppose there is an English teaching website and the website wants to help students check the similarity between two given sentences. Suggest an API for that purpose.'\n\n###\n\n","completion":" {'domain': 'NLP', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'roberta.large', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained RoBERTa model from PyTorch Hub. RoBERTa can be used to encode the sentences and compute the similarity by measuring the cosine distance between the encoded sentence vectors.\", 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/fairseq\", \"roberta.large\", pretrained=True)'}###"}
{"prompt":"Recommend me an API that can classify images of different types of food.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Densenet-169 model for image classification from PyTorch Hub, which can be fine-tuned for classifying different types of food.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet169', pretrained=True)'}###"}
{"prompt":"Design a food recognition system that can identify different kinds of food given photos. Recommend an API that can help achieve this.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Densenet-201 model from PyTorch Hub, which can be fine-tuned for food recognition and classification given images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'densenet201', pretrained=True)'}###"}
{"prompt":"Recommend a neural network API for semantic segmentation of images containing objects like cars, airplanes, and people.\n\n###\n\n","completion":" {'domain': 'Semantic Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet50 model from PyTorch Hub, which can perform semantic segmentation on images containing objects like cars, airplanes, and people.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet50', pretrained=True)'}###"}
{"prompt":"A social media platform is building an image classifier to identify and tag the content of images. Suggest an API that can help with this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'torch.hub.load(\"pytorch\/vision\", \"densenet161\", pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Utilize the pretrained DenseNet-161 model from the PyTorch library to classify and tag the content of images.', 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"densenet161\", pretrained=True)\nmodel.eval()' }###"}
{"prompt":"Help me find an API that can perform semantic segmentation on an image of San Francisco streets.\n\n###\n\n","completion":" {'domain': 'Image Segmentation', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained FCN-ResNet101 model from PyTorch Hub to perform semantic segmentation on an image of San Francisco streets.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'fcn_resnet101', pretrained=True)'}###"}
{"prompt":"Find me an API that can efficiently classify an image with low computational cost and memory access.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet model from PyTorch Hub for efficient image classification with low computational cost and memory access.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet39ds', pretrained=True)'}###"}
{"prompt":"Design a recommendation system for your video upload platform. Suggest an API that can detect and classify the subject from the image of the first frame.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet model for image classification from PyTorch Hub to detect and classify subjects from image frames.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68ds', pretrained=True)'}###"}
{"prompt":"Suggest an API specialized in image classification which can efficiently perform the task while consuming less memory and computational resources.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet model from PyTorch Hub, which is efficient in terms of memory usage and computational resources while providing high-quality image classification.', 'code': 'import torch\nmodel = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet68', pretrained=True)'}###"}
{"prompt":"Imagine you are creating an image recognition app. Select an API well-suited for this task, that is both fast and efficient.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('PingoLH\/Pytorch-HarDNet', 'hardnet85', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained HarDNet-85 model from PyTorch Hub, an efficient and fast model for image classification, suitable for an image recognition app.', 'code': 'import torch\nmodel = torch.hub.load(\"PingoLH\/Pytorch-HarDNet\", \"hardnet85\", pretrained=True)'}###"}
{"prompt":"A sports federation needs an AI-powered app that can recognize logos of soccer teams in a stadium. Give me an API that can output the name of the team, given a photo of its logo.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GhostNet model from PyTorch Hub, which can be fine-tuned for soccer team logo recognition given a photo of the logo.', 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/ghostnet', 'ghostnet_1x', pretrained=True)\"}###"}
{"prompt":"I want to create a website that allows users to upload images and classify what's in the image. Tell me an API that can perform image classification.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained GoogLeNet model for image classification from PyTorch Hub to identify and classify the contents of a user's uploaded image.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'googlenet', pretrained=True)'}###"}
{"prompt":"Advise an API for classifying images into their correct domain or appearance using PyTorch.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the ResNet-101-IBN-a model from PyTorch Hub for classifying images into their correct domain or appearance.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"I want to implement real-time face identification and facial recognition using the ResNet-50 model with IBN-Net. Recommend an appropriate API for the task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-50-IBN-a model from PyTorch Hub, which can be fine-tuned for real-time face identification and facial recognition.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'resnet50_ibn_a', pretrained=True)'}###"}
{"prompt":"I need an API that can classify pedestrian and vehicle images into their respective categories.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained IBN-Net model (SE-ResNet-101-IBN-a) from PyTorch Hub, a CNN model with domain\/appearance invariance, suitable for person\/vehicle re-identification tasks.', 'code': 'import torch\nmodel = torch.hub.load('XingangPan\/IBN-Net', 'se_resnet101_ibn_a', pretrained=True)'}###"}
{"prompt":"A company wants to classify images for their AI application, provide an API that can classify images based on the ImageNet dataset.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load(repo_or_dir='pytorch\/vision', model='inception_v3', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Inception_v3 model from PyTorch Hub, which is trained on the ImageNet dataset, to classify images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'inception_v3', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"A wildlife conservatory organization wants to classify different species of birds using image recognition. Suggest an API that can classify high-resolution bird images.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt-101 model from PyTorch Hub for high-resolution image classification, which can be fine-tuned to classify bird species in wildlife images.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest101', pretrained=True)'}###"}
{"prompt":"Can you suggest an API that can classify images into different categories like animals, objects, etc.?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNeSt model from PyTorch Hub for image classification into diverse categories, such as animals and objects.', 'code': 'import torch\nmodel = torch.hub.load('zhanghang1989\/ResNeSt', 'resnest269', pretrained=True)'}###"}
{"prompt":"Recommend me an API that can optimize image classification for varying hardware platforms.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_cpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ProxylessNAS model from PyTorch Hub, optimized for different hardware platforms to improve image classification performance.\", 'code': 'import torch\ntarget_platform = \"proxylessnas_cpu\"\nmodel = torch.hub.load(\"mit-han-lab\/ProxylessNAS\", target_platform, pretrained=True)'}###"}
{"prompt":"I work at a company that needs an optimized model for image classification on GPU with high accuracy. Can you suggest a suitable API for me?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'For an optimized image classification model on GPU with high accuracy, consider using the pre-trained ProxylessNAS model from the MIT-HAN lab.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_gpu', pretrained=True)'}###"}
{"prompt":"I want an API that can handle object detection on mobile devices. It should be light-weight and efficient.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ProxylessNAS Mobile model, optimized for object detection on mobile devices. It is lightweight and efficient.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/ProxylessNAS', 'proxylessnas_mobile', pretrained=True)'}###"}
{"prompt":"Can you suggest me an API that can classify objects from images?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet50 model from PyTorch Hub, designed for image classification tasks, including object recognition in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet50', pretrained=True)\nmodel.eval()'}###"}
{"prompt":"A software engineer at TikTok is developing a recommendation system for videos. He needs an API that can recognize content of an image associated with a video.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet18', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained ResNet18 model from PyTorch Hub for image classification, which can be used to recognize the content of an image associated with a video.\", 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"resnet18\", pretrained=True)\nmodel.eval()'}###"}
{"prompt":"I am building an image identification service that recognizes objects in images. I need an API suitable for this task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pre-trained ResNet34 model from PyTorch Hub to perform object recognition in images.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet34', pretrained=True)\"}###"}
{"prompt":"Build an API to identify the object in the image by its class.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet model from PyTorch Hub to classify objects in images and identify their classes.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet152', pretrained=True)'}###"}
{"prompt":"I want an API to label which type of object is in a given picture: car, dog, tree or house. Provide an API that can do this assignment.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNet-101 model from PyTorch Hub, which can be fine-tuned for object classification into car, dog, tree, or house.', 'code': \"import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnet101', pretrained=True)\"}###"}
{"prompt":"An image classification tool needs to be integrated into a real time webcam application that can detect different objects. Suggest me an API that can do this with low latency for a real time application.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MobileNet v2 model for image classification from PyTorch Hub to perform object detection in real-time webcam applications with low latency.', 'code': 'import torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\n\nmodel = torch.hub.load('pytorch\/vision', 'mobilenet_v2', pretrained=True)\nmodel.eval()\n\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])'}###"}
{"prompt":"We are developing an image-based plant disease identification system. Give me an image classification API that yields high accuracy and can be fine-tuned to our task.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'resnext101_32x8d', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ResNext101_32x8d model from PyTorch Hub, which provides high accuracy and can be fine-tuned for plant disease identification.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'resnext101_32x8d', pretrained=True)'}###"}
{"prompt":"Suggest me a powerful image classification API for efficient image classification with decent accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the SNNMLP model from PyTorch Hub with pre-trained weights for efficient image classification with high accuracy.', 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_s', pretrained=True)'}###"}
{"prompt":"We're at Huawei Labs and building an image classification model that does not have extra floating-point operations. Suggest an API that does this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SNNMLP Base model from PyTorch Hub, which incorporates LIF neurons and achieves better accuracy without additional FLOPs for image classification.\", 'code': 'import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_b', pretrained=True)'}###"}
{"prompt":"Recommend me a model that can classify objects within images without increasing computational complexity considerably.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the SNNMLP Tiny model from PyTorch Hub, which incorporates LIF neurons into MLP models for better accuracy without extra FLOPs, making it efficient for object classification.\", 'code': \"import torch\nmodel = torch.hub.load('huawei-noah\/Efficient-AI-Backbones', 'snnmlp_t', pretrained=True)\"}###"}
{"prompt":"Find me an AI model capable of classifying images efficiently with fewer parameters but still maintaining high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained SqueezeNet model from PyTorch Hub. SqueezeNet is an image classification model that achieves competitive accuracy with fewer parameters, making it efficient for use.\", 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_0', pretrained=True)'}###"}
{"prompt":"Can you recommend an API that can classify images based on their content and requires less computational resources compared to AlexNet?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'SqueezeNet is an image classification model that has fewer parameters and requires less computation compared to AlexNet while maintaining similar accuracy.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'squeezenet1_1', pretrained=True)'}###"}
{"prompt":"Provide me an API that can efficiently classify images into different categories.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': 'model = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained ShuffleNet v2 model from PyTorch Hub, optimized for speed and memory, to efficiently classify images into different categories.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'shufflenet_v2_x1_0', pretrained=True)'}###"}
{"prompt":"Recommend an API to classify artwork images into categories like Impressionism, Cubism, and Surrealism.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model from PyTorch Hub, which can be fine-tuned for artwork classification into categories like Impressionism, Cubism, and Surrealism.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11', pretrained=True)'}###"}
{"prompt":"\"A client wants to recognize different types of plant diseases from an image. Suggest an appropriate machine learning API that can accomplish this.\"\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': 'model = torch.hub.load(\\'pytorch\/vision\\', \\'vgg11_bn\\', pretrained=True)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG11 model with batch normalization using PyTorch Hub, which can be fine-tuned for plant disease recognition from image data.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg11_bn', pretrained=True)'}###"}
{"prompt":"What is an API that I can use to identify what kind of food is in an image?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model with batch normalization from PyTorch Hub, which can be fine-tuned for food identification in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13_bn', pretrained=True)'}###"}
{"prompt":"Describe an API that can identify different breeds of dogs in a given image.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 model from PyTorch Hub, which can be fine-tuned to identify different dog breeds in a given image.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19', pretrained=True)'}###"}
{"prompt":"An art gallery is interested in recognizing artwork styles in photographs. Advise them an API to accomplish this task.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG19 with batch normalization from PyTorch Hub, which can be fine-tuned for artwork style recognition in photographs.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg19_bn', pretrained=True)'}###"}
{"prompt":"I need to find an API to recognize objects in photos, what would you suggest?\n\n###\n\n","completion":" {'domain': 'Image Recognition', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG-16 model from PyTorch Hub for object recognition in images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg16', pretrained=True)'}###"}
{"prompt":"An online glasses store needs an AI tool to categorize thousands of glasses into their different styles. Provide a suitable image recognition API for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained VGG13 model from PyTorch Hub, which can be fine-tuned for the categorization of different glasses styles in an online store.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'vgg13', pretrained=True)'}###"}
{"prompt":"Find me a deep learning API that can recognize images of cars and their types from large datasets like Imagenet.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'vgg16_bn', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained VGG16 model with batch normalization from PyTorch Hub, which is suitable for large-scale image recognition tasks like recognizing car types from the ImageNet dataset.\", 'code': 'import torch\nmodel = torch.hub.load(\"pytorch\/vision\", \"vgg16_bn\", pretrained=True)'}###"}
{"prompt":"Suggest an optimal API for building an image recognition system for a company that demands high accuracy.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-50 model for image recognition, offering high accuracy for various image classification tasks.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet50_2', pretrained=True)'}###"}
{"prompt":"What API should I use for my drone's object detertion that classify objects from images using Wide Residual Networks?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained Wide ResNet-101-2 model from PyTorch Hub for image classification in object detection scenarios on drone-acquired images.', 'code': 'import torch\nmodel = torch.hub.load('pytorch\/vision', 'wide_resnet101_2', pretrained=True)'}###"}
{"prompt":"A video analyst is working on recognizing sports clips to create a promotional video. Which API should they use to classify actions in the video?\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'slowfast_r50', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': \"Load the SlowFast Networks model from PyTorchVideo, which is pretrained on the Kinetics 400 dataset and able to classify actions in a video.\", 'code': 'import torch\nmodel = torch.hub.load(\\'facebookresearch\/pytorchvideo\\', \\'slowfast_r50\\', pretrained=True)'}###"}
{"prompt":"I want to create a program that analyzes CCTV footage and classifies suspicious activities. Help me with an API that can do this.\n\n###\n\n","completion":" {'domain': 'Video Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\", 'api_provider': 'PyTorchVideo', 'explanation': \"Load the pretrained X3D model from PyTorchVideo Hub for video classification, which can help identify suspicious activities in CCTV footage.\", 'code': \"import torch\nmodel = torch.hub.load('facebookresearch\/pytorchvideo', 'x3d_s', pretrained=True)\"}###"}
{"prompt":"Find me an API that helps in classifying images based on their content using models trained with limited labeled data.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained ResNet-18 model from Facebook Research's Semi-Supervised ImageNet1K Models' collection, designed for image classification using models trained with limited labeled data.\", 'code': 'import torch\nmodel = torch.hub.load('facebookresearch\/semi-supervised-ImageNet1K-models', 'resnet18_swsl', pretrained=True)'}###"}
{"prompt":"I am building an app for classifying pet images into dog breeds using neural networks. Can you recommend a suitable API to use?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained OFA SuperNet MobileNetV3 from PyTorch Hub, which can be fine-tuned for classifying pet images into different dog breeds.', 'code': 'import torch\nmodel = torch.hub.load('mit-han-lab\/once-for-all', 'ofa_supernet_mbv3_w10', pretrained=True)'}###"}
{"prompt":"I work at NVIDIA and want to find an API which can help me classify images based on their content using a GPU-optimized neural network.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': 'gpunet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)', 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained GPUNet model from NVIDIA PyTorch Hub to classify images. This model is optimized for NVIDIA GPU and TensorRT performance.', 'code': 'import torch\nmodel_type = 'GPUNet-0'\nprecision = 'fp32'\ngpunet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_gpunet', pretrained=True, model_type=model_type, model_math=precision)'}###"}
{"prompt":"Find me an API that can tokenize English text and perform NLP tasks like text summarization, translation, or question answering.\n\n###\n\n","completion":" {'domain': 'Natural Language Processing', 'api_call': 'tokenizer = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'tokenizer\\', \\'bert-base-cased\\')\nmodel = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'model\\', \\'bert-base-cased\\')', 'api_provider': 'PyTorch', 'explanation': 'Use the PyTorch-Transformers library by Hugging Face, which provides pre-trained models like BERT, GPT, and RoBERTa for tokenization and NLP tasks such as summarization, translation, and question answering.', 'code': 'import torch\ntokenizer = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'tokenizer\\', \\'bert-base-cased\\')\nmodel = torch.hub.load(\\'huggingface\/pytorch-transformers\\', \\'model\\', \\'bert-base-cased\\')'}###"}
{"prompt":"A language teacher wants to check the grammar of some sentences by translating them back and forth between languages. Provide an API that can handle neural machine translation for this purpose.\n\n###\n\n","completion":" {'domain': 'Natural Language Processing', 'api_call': \"model = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained Transformer (NMT) model from PyTorch Hub, which can handle neural machine translation. This will help the language teacher to check grammar by translating sentences back and forth between languages.\", 'code': \"import torch\nmodel = torch.hub.load('pytorch\/fairseq', 'transformer.wmt14.en-fr', tokenizer='moses', bpe='subword_nmt')\"}###"}
{"prompt":"I'm looking for an API that can quickly classify images with high accuracy. What do you suggest?\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained EfficientNet model from PyTorch Hub, which provides state-of-the-art accuracy while being smaller and faster for image classification tasks.', 'code': \"import torch\nefficientnet = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)\"}###"}
{"prompt":"Can you recommend an API to ensure the safety of autonomous cars by detecting pedestrians, cars and other objects in a timely manner?\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained NVIDIA SSD (Single Shot MultiBox Detector) model from PyTorch Hub to detect pedestrians, cars, and other objects in images, ensuring autonomous vehicles' safety.\", 'code': \"import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_ssd', pretrained=True)\"}###"}
{"prompt":"I'm looking for an API that can provide me a deep learning model to convert text to speech with human-like voice quality.\n\n###\n\n","completion":" {'domain': 'Speech Synthesis', 'api_call': \"model = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp16')\", 'api_provider': 'PyTorch', 'explanation': \"Load NVIDIA's Tacotron 2 model from PyTorch Hub, which is designed to convert text to human-like speech.\", 'code': 'import torch\nmodel = torch.hub.load('NVIDIA\/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math=\"fp16\")'}###"}
{"prompt":"I desire a robust image classifier for my online art school. Can you provide me with an API that can classify images?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model for image classification from PyTorch Hub to classify images efficiently and accurately.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50', pretrained=True)\"}###"}
{"prompt":"I want to identify the main object in a given image. Which API would help me in doing this?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub, which is capable of identifying the main object in an image by classifying it into one of the ImageNet classes.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"I need an API to classify images on the ImageNet dataset without using any additional tricks. Please suggest a suitable one.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model for image classification on the ImageNet dataset. It achieves state-of-the-art results without tricks like architecture modification or additional outside training data.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_cutmix', pretrained=True)\"}###"}
{"prompt":"My company is building a tool to automatically classify images using AI. Tell me about an API that can be used for this purpose.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_075', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'To automatically classify images, use the MEAL V2 pre-trained model for image classification from PyTorch Hub.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', 'mealv2_mobilenetv3_small_075', pretrained=True)\"}###"}
{"prompt":"Recommend an image classifier API to develop a phone app that can identify and tag objects in photos.\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the pretrained MEAL V2 model from PyTorch Hub, which can identify and tag objects in photos with high accuracy.', 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_mobilenet_v3_large_100', pretrained=True)\"}###"}
{"prompt":"I want to classify an image of a dog for a contest. Can you recommend me an API for accurately doing this task?\n\n###\n\n","completion":" {'domain': 'Image Classification', 'api_call': \"model = torch.hub.load('szq0214\/MEAL-V2', 'meal_v2', model='mealv2_resnest50_380x380', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MEAL V2 model from PyTorch Hub for highly accurate image classification, which can be used to classify the dog's image for the contest.\", 'code': \"import torch\nmodel = torch.hub.load('szq0214\/MEAL-V2','meal_v2', 'mealv2_resnest50_380x380', pretrained=True)\"}###"}