TorchVision Datasets

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Overview

TorchVision's datasets module offers a collection of built-in datasets, streamlining the process of accessing and using common datasets for computer vision tasks. These datasets are subclasses of `torch.utils.data.Dataset`, making them compatible with PyTorch's data loading utilities. They support transformations and target transformations for pre-processing. Datasets cover image classification (e.g., ImageNet, CIFAR), object detection/segmentation (e.g., COCO, Pascal VOC), optical flow (e.g., FlyingChairs), and stereo matching. Utility classes are provided to assist in creating custom datasets. Multi-processing data loading is supported through `torch.utils.data.DataLoader`. The module ensures efficient access, pre-processing, and integration with PyTorch workflows for research and development in computer vision. The system downloads and extracts the dataset, and a dummy dataset is recommended in distributed setting. Datasets are structured for tasks ranging from simple image recognition to advanced scene understanding and 3D reconstruction.

Common tasks

Image Classification Object Detection Semantic Segmentation Optical Flow Estimation Stereo Matching

FAQ

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What types of datasets are available in TorchVision?

TorchVision provides datasets for image classification, object detection, semantic segmentation, optical flow, and stereo matching.

How do I load a dataset using TorchVision?

You can load a dataset by importing the `torchvision.datasets` module, selecting the desired dataset, specifying the root directory, and creating a `torch.utils.data.DataLoader`.

Can I apply transformations to the data?

Yes, TorchVision supports applying a wide range of transformations using the `torchvision.transforms` module.

How can I create my own custom dataset?

You can create a custom dataset by extending the `torch.utils.data.Dataset` class and implementing the `__len__` and `__getitem__` methods.

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