Transforming and augmenting images Torchvision supports common computer vision transformations in the torchvision. Torchvision’s V2 image transforms support annotations for various tasks, such as bounding boxes for object detection and segmentation masks for image segmentation. v2 enables jointly transforming images, videos, bounding boxes, and masks. The following objects are supported: Images as pure tensors, Image or PIL image Videos as Video Axis-aligned and rotated bounding boxes as BoundingBoxes Segmentation Dec 14, 2025 · v2 (Modern): Type-aware transformations with kernel registry and metadata preservation via tv_tensors System Architecture The transforms system consists of three primary components: the v1 legacy API, the v2 modern API with kernel dispatch, and the tv_tensors metadata system. First, a bit VisionTransformer The VisionTransformer model is based on the An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale paper. Read more in in our docs. The following objects are supported: Images as pure tensors, Image or PIL image Videos as Video Axis-aligned and rotated bounding boxes as BoundingBoxes Getting started with transforms v2 Note Try on Colab or go to the end to download the full example code. They are now 1 Getting started with transforms v2 Note Try on Colab or go to the end to download the full example code. We walk through the process of building an augmentation pipeline, applying MixUp and CutMix, designing a modern CNN with attention How to write your own v2 transforms Note Try on Colab or go to the end to download the full example code. Compose is a simple callable class which allows us to do this.

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