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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Alzheimer stage classification | ADNI | AUC65.9 | 116 | |
| AD diagnosis | ADNI (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 Classification | AIBL | mAUC85.16 | 6 | |
| Multi-class Alzheimer's Disease Classification | OASIS | mAUC0.6649 | 6 |
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