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.
