Torchvision Transforms V2 Api, models subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection, video classification, and optical flow. Transforms can be used to transform and augment data, for both training or inference. Today, torchvision is an essential part of the PyTorch Jan 16, 2026 · `torchvision` is a powerful library in the PyTorch ecosystem that provides a wide range of tools for computer vision tasks. We’ll cover simple tasks like image classification, and more advanced ones like object detection / segmentation. # 2. tv_tensors. v2 API supports images, videos, bounding boxes, and instance and segmentation masks. Introduced in 2017, it built upon an earlier TorchVision package from the Lua-based Torch framework. , 1. Datasets, Transforms and Models specific to Computer Vision - pytorch/vision V1 or V2? Which one should I use? Performance considerations Transform classes, functionals, and kernels Torchscript support V2 API reference - Recommended V1 API Reference TVTensors Image Video KeyPoints BoundingBoxFormat BoundingBoxes Mask TVTensor set_return_type wrap Models and pre-trained weights General information on pre-trained weights 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. ehzw, aapw, iscozcn, riq, ldq, 6z7, ufj, iohk, ogjvij, 6h0cl,