June 4, 2020

245 words 2 mins read



PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

repo name facebookresearch/SlowFast
repo link https://github.com/facebookresearch/SlowFast
language Python
size (curr.) 4975 kB
stars (curr.) 2505
created 2019-08-20
license Apache License 2.0


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



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 INSTALL.md. You may follow the instructions in DATASET.md to prepare the datasets.

Quick Start

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


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

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