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README
Apache-2.0

简介

English | 简体中文

Documentation actions codecov PyPI LICENSE Average time to resolve an issue Percentage of issues still open

MMAction2 是一款基于 PyTorch 的视频理解开源工具箱,是 OpenMMLab 项目的成员之一

主分支代码目前支持 PyTorch 1.3 以上的版本


Kinetics-400 上的动作识别

AVA-2.1 上的时空动作检测

NTURGB+D-120 上的基于人体姿态的动作识别

主要特性

  • 模块化设计 MMAction2 将统一的视频理解框架解耦成不同的模块组件,通过组合不同的模块组件,用户可以便捷地构建自定义的视频理解模型

  • 支持多样的数据集 MMAction2 支持多种数据集的准备和构建,包括 UCF101, Kinetics-[400/600/700], Something-Something V1&V2, Moments in Time, Multi-Moments in Time, THUMOS14 等

  • 支持多种视频理解任务 MMAction2 支持多种主流的视频理解任务

    • 动作识别:TSN, TSM, TIN, R(2+1)D, I3D, SlowOnly, SlowFast, CSN, Non-local 等
    • 时序动作检测:BSN, BMN, SSN
    • 时空动作检测:SlowOnly + Fast-RCNN, SlowFast + Fast-RCNN

    具体可参考 模型库

  • 详尽的单元测试和文档 MMAction2 提供了详尽的说明文档,API 接口说明,全面的单元测试,以供社区参考

更新记录

v0.18.0 版本已于 2021 年 9 月 2 日发布,可通过查阅 更新日志 了解更多细节以及发布历史

基准测试

Model input io backend batch size x gpus MMAction2 (s/iter) MMAction (s/iter) Temporal-Shift-Module (s/iter) PySlowFast (s/iter)
TSN 256p rawframes Memcached 32x8 0.32 0.38 0.42 x
TSN 256p dense-encoded video Disk 32x8 0.61 x x TODO
I3D heavy 256p videos Disk 8x8 0.34 x x 0.44
I3D 256p rawframes Memcached 8x8 0.43 0.56 x x
TSM 256p rawframes Memcached 8x8 0.31 x 0.41 x
Slowonly 256p videos Disk 8x8 0.32 TODO x 0.34
Slowfast 256p videos Disk 8x8 0.69 x x 1.04
R(2+1)D 256p videos Disk 8x8 0.45 x x x

更多详情可见 基准测试

模型库

支持的行为识别方法:

(点击收起)

支持的时序动作检测方法:

(点击收起)
  • BSN (ECCV'2018)
  • BMN (ICCV'2019)
  • SSN (ICCV'2017)

支持的时空检测方法:

(点击收起)

支持的基于骨骼点的动作识别方法:

(点击收起)

各个模型的结果和设置都可以在对应的 config 目录下的 README_zh-CN.md 中查看。整体的概况也可也在 模型库 页面中查看

我们将跟进学界的最新进展,并支持更多算法和框架。如果您对 MMAction2 有任何功能需求,请随时在 问题 中留言。

数据集

支持的 数据集

支持的动作识别数据集:

(点击收起)

支持的时序动作检测数据集:

(点击收起)

支持的时空动作检测数据集:

(点击收起)

标记 🔲 代表对应数据集并未被完全支持,但提供相应的数据准备步骤。

安装

请参考 安装指南 进行安装

数据集准备

请参考 数据准备 了解数据集准备概况。所有支持的数据集都列于 数据集清单

教程

请参考 基础教程 了解 MMAction2 的基本使用。MMAction2也提供了其他更详细的教程:

MMAction2 也提供了相应的中文 Colab 教程,可以点击 这里 进行体验!

常见问题

请参考 FAQ 了解其他用户的常见问题

许可

该项目开源自 Apache 2.0 license

引用

如果你觉得 MMAction2 对你的研究有所帮助,可以考虑引用它:

@misc{2020mmaction2,
    title={OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark},
    author={MMAction2 Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmaction2}},
    year={2020}
}

参与贡献

我们非常欢迎用户对于 MMAction2 做出的任何贡献,可以参考 贡献指南 文件了解更多细节

致谢

MMAction2 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者,以及提供宝贵反馈的用户。 我们希望该工具箱和基准测试可以为社区提供灵活的代码工具,供用户复现现有算法并开发自己的新模型,从而不断为开源社区提供贡献。

OpenMMLab 的其他项目

  • MMCV: OpenMMLab 计算机视觉基础库
  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMClassification: OpenMMLab 图像分类工具箱与测试基准
  • MMDetection: OpenMMLab 检测工具箱与测试基准
  • MMDetection3D: OpenMMLab 新一代通用3D目标检测平台
  • MMSegmentation: OpenMMLab 语义分割工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱与测试基准
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMPose: OpenMMLab 姿态估计工具箱与测试基准
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具包
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱

欢迎加入 OpenMMLab 社区

扫描下方的二维码可关注 OpenMMLab 团队的 知乎官方账号,加入 OpenMMLab 团队的 官方交流 QQ 群

我们会在 OpenMMLab 社区为大家

  • 📢 分享 AI 框架的前沿核心技术
  • 💻 解读 PyTorch 常用模块源码
  • 📰 发布 OpenMMLab 的相关新闻
  • 🚀 介绍 OpenMMLab 开发的前沿算法
  • 🏃 获取更高效的问题答疑和意见反馈
  • 🔥 提供与各行各业开发者充分交流的平台

干货满满 📘,等你来撩 💗,OpenMMLab 社区期待您的加入 👬

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