Skip to content
Switch branches/tags

Latest commit

Summary: Pull Request resolved: #426

Reviewed By: bxiong1202, feichtenhofer

Differential Revision: D29041986

Pulled By: haooooooqi

fbshipit-source-id: 2ac987eceeedec66cf4a7d332134556a79df9091

Git stats


Failed to load latest commit information.


PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficient training. This repository includes implementations of the following methods:


The goal of PySlowFast is to provide a high-performance, light-weight pytorch codebase provides state-of-the-art video backbones for video understanding research on different tasks (classification, detection, and etc). It is designed in order to support rapid implementation and evaluation of novel video research ideas. PySlowFast includes implementations of the following backbone network architectures:

  • SlowFast
  • Slow
  • C2D
  • I3D
  • Non-local Network
  • X3D



PySlowFast is released under the Apache 2.0 license.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the PySlowFast Model Zoo.


Please find installation instructions for PyTorch and PySlowFast in You may follow the instructions in to prepare the datasets.

Quick Start

Follow the example in to start playing video models with PySlowFast.

Visualization Tools

We offer a range of visualization tools for the train/eval/test processes, model analysis, and for running inference with trained model. More information at Visualization Tools.


PySlowFast is written and maintained by Haoqi Fan, Yanghao Li, Bo Xiong, Wan-Yen Lo, Christoph Feichtenhofer.

Citing PySlowFast

If you find PySlowFast useful in your research, please use the following BibTeX entry for citation.

  author =       {Haoqi Fan and Yanghao Li and Bo Xiong and Wan-Yen Lo and
                  Christoph Feichtenhofer},
  title =        {PySlowFast},
  howpublished = {\url{}},
  year =         {2020}