PyTorchVideo: A Deep Learning Library for Video Understanding
Haoqi Fan
Tullie Murrell
Heng Wang
Kalyan Vasudev Alwala
Yanghao Li
Yilei Li
Bo Xiong
Nikhila Ravi
Meng Li
Haichuan Yang
Jitendra Malik
Ross B. Girshick
Matt Feiszli
Aaron B. Adcock
Wan-Yen Lo
Christoph Feichtenhofer

Abstract
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/
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