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PyTorchVideo: A Deep Learning Library for Video Understanding

About

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/

Haoqi Fan, Tullie Murrell, Heng Wang, Kalyan Vasudev Alwala, Yanghao Li, Yilei Li, Bo Xiong, Nikhila Ravi, Meng Li, Haichuan Yang, Jitendra Malik, Ross Girshick, Matt Feiszli, Aaron Adcock, Wan-Yen Lo, Christoph Feichtenhofer• 2021

Related benchmarks

TaskDatasetResultRank
Alzheimer stage classificationADNI
AUC65.9
116
AD diagnosisADNI (test)--
16
Binary Alzheimer's Disease Classification (CN vs. AD)OASIS (test)
AUC80.22
13
Binary Alzheimer's Disease Classification (CN vs. AD)AIBL (test)
AUC93.95
13
Multi-class Alzheimer's Disease ClassificationAIBL
mAUC85.16
6
Multi-class Alzheimer's Disease ClassificationOASIS
mAUC0.6649
6
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