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README
Apache-2.0

English | 简体中文

简介

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 1.5 以上的版本


Kinetics-400 上的动作识别


NTURGB+D-120 上的基于人体姿态的动作识别


Kinetics-400 上的基于 skeleton 的时空动作检测和动作识别


AVA-2.1 上的时空动作检测

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

  • 支持多种任务和数据集:MMAction2 支持多种视频理解任务,包括动作识别,时序动作检测,时空动作检测以及基于人体姿态的动作识别,总共支持 27 种算法和 20 种数据集

  • 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

🌟 1.x 预览版本

全新的 MMAction2 v1.0.0rc0 版本已经在 2022.09.01 发布:

  • 基于 MMEngine 统一了各组件接口。
  • 全面支持混合精度,训练测试速度更快。
  • 更加灵活的架构

欢迎在 1.x branch 发现更多的新特性。欢迎 issue 和 PR。

💎 稳定版本

  • (2022-03-04) 在 K400 上支持 Multigrid 训练,达到 76.07% 的识别准确率并加快了训练速度。
  • (2021-11-24) 在 NTU60 XSub 上支持 2s-AGCN, 在 joint stream 和 bone stream 上分别达到 86.06% 和 86.89% 的识别准确率。
  • (2021-10-29) 支持基于 skeleton 模态和 rgb 模态的时空动作检测和行为识别 demo (demo/demo_video_structuralize.py)。
  • (2021-10-26) 在 NTU60 3d 关键点标注数据集上训练测试 STGCN, 可达到 84.61% (高于 paper 中的 81.5%) 的识别准确率。
  • (2021-10-25) 提供将 NTU60 和 NTU120 的 3d 骨骼点数据转换成我们项目的格式的脚本(tools/data/skeleton/gen_ntu_rgbd_raw.py)。
  • (2021-10-25) 提供使用自定义数据集训练 PoseC3D 的 教程,此 PR 由用户 bit-scientist 完成!
  • (2021-10-16) 在 UCF101, HMDB51 上支持 PoseC3D,仅用 2D 关键点就可分别达到 87.0% 和 69.3% 的识别准确率。两数据集的预提取骨架特征可以公开下载。

v0.24.0 版本已于 2022 年 5 月 5 日发布,可通过查阅更新日志 了解更多细节以及发布历史

安装

MMAction2 依赖 PyTorch, MMCV, MMDetection(可选), MMPose(可选),以下是安装的简要步骤。 更详细的安装指南请参考 install.md

conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet  # 可选
mim install mmpose  # 可选
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip3 install -e .

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!

模型库

行为识别方法
C3D (CVPR'2014) TSN (ECCV'2016) I3D (CVPR'2017) I3D Non-Local (CVPR'2018) R(2+1)D (CVPR'2018)
TRN (ECCV'2018) TSM (ICCV'2019) TSM Non-Local (ICCV'2019) SlowOnly (ICCV'2019) SlowFast (ICCV'2019)
CSN (ICCV'2019) TIN (AAAI'2020) TPN (CVPR'2020) X3D (CVPR'2020) OmniSource (ECCV'2020)
MultiModality: Audio (ArXiv'2020) TANet (ArXiv'2020) TimeSformer (ICML'2021)
时序动作检测方法
SSN (ICCV'2017) BSN (ECCV'2018) BMN (ICCV'2019)
时空动作检测方法
ACRN (ECCV'2018) SlowOnly+Fast R-CNN (ICCV'2019) SlowFast+Fast R-CNN (ICCV'2019) LFB (CVPR'2019)
基于骨骼点的动作识别方法
ST-GCN (AAAI'2018) 2s-AGCN (CVPR'2019) PoseC3D (ArXiv'2021)

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

MMAction2 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

动作识别数据集
HMDB51 (主页) (ICCV'2011) UCF101 (主页) (CRCV-IR-12-01) ActivityNet (主页) (CVPR'2015) Kinetics-[400/600/700] (主页) (CVPR'2017)
SthV1 (主页) (ICCV'2017) SthV2 (主页) (ICCV'2017) Diving48 (主页) (ECCV'2018) Jester (主页) (ICCV'2019)
Moments in Time (主页) (TPAMI'2019) Multi-Moments in Time (主页) (ArXiv'2019) HVU (主页) (ECCV'2020) OmniSource (主页) (ECCV'2020)
FineGYM (主页) (CVPR'2020)
时序动作检测数据集
THUMOS14 (主页) (THUMOS Challenge 2014) ActivityNet (主页) (CVPR'2015)
时空动作检测数据集
UCF101-24* (主页) (CRCV-IR-12-01) JHMDB* (主页) (ICCV'2015) AVA (主页) (CVPR'2018)
基于骨骼点的动作识别数据集
PoseC3D-FineGYM (主页) (ArXiv'2021) PoseC3D-NTURGB+D (主页) (ArXiv'2021) PoseC3D-UCF101 (主页) (ArXiv'2021) PoseC3D-HMDB51 (主页) (ArXiv'2021)

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。整体的概况也可也在 数据集 页面中查看

基准测试

为了验证 MMAction2 框架的高精度和高效率,开发成员将其与当前其他主流框架进行速度对比。更多详情可见 基准测试

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

常见问题

请参考 FAQ 了解其他用户的常见问题

相关工作

目前有许多研究工作或工程项目基于 MMAction2 搭建,例如:

  • Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 Oral. [论文][代码]
  • Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 Oral. [论文][代码]
  • Video Swin Transformer. [论文][代码]

更多详情可见 相关工作

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。

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}
}

许可

该项目开源自 Apache 2.0 license

OpenMMLab 的其他项目

  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMClassification: OpenMMLab 图像分类工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMYOLO: OpenMMLab YOLO 系列工具箱和基准测试
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具箱
  • MMPose: OpenMMLab 姿态估计工具箱
  • MMHuman3D: OpenMMLab 人体参数化模型工具箱与测试基准
  • MMSelfSup: OpenMMLab 自监督学习工具箱与测试基准
  • MMRazor: OpenMMLab 模型压缩工具箱与测试基准
  • MMFewShot: OpenMMLab 少样本学习工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMFlow: OpenMMLab 光流估计工具箱与测试基准
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架

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  • 📢 分享 AI 框架的前沿核心技术
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干货满满 📘,等你来撩 💗,OpenMMLab 社区期待您的加入 👬

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简介

基于 PyTorch 和 MMCV 的视频理解工具库,支持动作识别、动作定位、时空动作检测和骨骼动作识别等多种任务。 展开 收起
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