Skip to content

/ SlowFast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
Python Shell
  1. Python 99.9%
  2. Shell 0.1%
Branch: master
Find file
Clone or download

Clone with HTTPS

Use Git or checkout with SVN using the web URL.

Open in Desktop Download ZIP

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio

If nothing happens, download the GitHub extension for Visual Studio and try again.

@facebook-github-bot
facebook-github-bot Initial commit 
fbshipit-source-id: 2b598ec47ac40a9bb7a9a35bfb5bdc8e711b9e1b
Latest commit 31d2ea7 Jan 1, 2020
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
configs Initial commit Dec 31, 2019
demo Initial commit Dec 31, 2019
slowfast Initial commit Dec 31, 2019
tools Initial commit Dec 31, 2019
CODE_OF_CONDUCT.md Initial commit Dec 31, 2019
CONTRIBUTING.md Initial commit Dec 31, 2019
GETTING_STARTED.md Initial commit Dec 31, 2019
INSTALL.md Initial commit Dec 31, 2019
LICENSE Initial commit Dec 31, 2019
MODEL_ZOO.md Initial commit Dec 31, 2019
README.md Initial commit Dec 31, 2019
linter.sh Initial commit Dec 31, 2019
setup.cfg Initial commit Dec 31, 2019
setup.py Initial commit Dec 31, 2019

README.md

PySlowFast

PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models, including papers "SlowFast Networks for Video Recognition", and "Non-local Neural Networks".

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
  • SlowOnly
  • C2D
  • I3D
  • Non-local Network

Updates

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.

You can’t perform that action at this time.