PySlowFast
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:
- SlowFast Networks for Video Recognition
- Non-local Neural Networks
- A Multigrid Method for Efficiently Training Video Models
- X3D: Progressive Network Expansion for Efficient Video Recognition
Introduction
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
Updates
- We now support X3D Models. See
projects/x3dfor more information. - We now support Multigrid Training for efficiently training video models. See
projects/multigridfor more information. - PySlowFast is released in conjunction with our ICCV 2019 Tutorial.
License
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.
Installation
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.
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.
Contributors
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.
@misc{fan2020pyslowfast,
author = {Haoqi Fan and Yanghao Li and Bo Xiong and Wan-Yen Lo and
Christoph Feichtenhofer},
title = {PySlowFast},
howpublished = {\url{https://github.com/facebookresearch/slowfast}},
year = {2020}
}