+
+Datasets marked with * are not fully supported yet, but related dataset preparation steps are provided. A summary can be found on the [**Supported Datasets**](https://mmaction2.readthedocs.io/en/latest/supported_datasets.html) page.
+
+## Benchmark
+
+To demonstrate the efficacy and efficiency of our framework, we compare MMAction2 with some other popular frameworks and official releases in terms of speed. Details can be found in [benchmark](docs/benchmark.md).
+
+## Data Preparation
+
+Please refer to [data_preparation.md](docs/data_preparation.md) for a general knowledge of data preparation.
+The supported datasets are listed in [supported_datasets.md](docs/supported_datasets.md)
+
+## FAQ
+
+Please refer to [FAQ](docs/faq.md) for frequently asked questions.
+
+## Projects built on MMAction2
+
+Currently, there are many research works and projects built on MMAction2 by users from community, such as:
+
+- Video Swin Transformer. [\[paper\]](https://arxiv.org/abs/2106.13230)[\[github\]](https://github.com/SwinTransformer/Video-Swin-Transformer)
+- Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 **Oral**. [\[paper\]](https://arxiv.org/abs/2107.10161)[\[github\]](https://github.com/Cogito2012/DEAR)
+- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [\[paper\]](https://arxiv.org/abs/2103.17263)[\[github\]](https://github.com/xvjiarui/VFS)
+
+etc., check [projects.md](docs/projects.md) to see all related projects.
+
+## Contributing
+
+We appreciate all contributions to improve MMAction2. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.
+
+## Acknowledgement
+
+MMAction2 is an open-source project that is contributed by researchers and engineers from various colleges and companies.
+We appreciate all the contributors who implement their methods or add new features and users who give valuable feedback.
+We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their new models.
+
+## Citation
+
+If you find this project useful in your research, please consider cite:
+
+```BibTeX
+@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}
+}
+```
+
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
+## Projects in OpenMMLab
+
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
diff --git a/openmmlab_test/mmaction2-0.24.1/README_zh-CN.md b/openmmlab_test/mmaction2-0.24.1/README_zh-CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..d6a1e2af43706e622150edd63eabf71c8daea976
--- /dev/null
+++ b/openmmlab_test/mmaction2-0.24.1/README_zh-CN.md
@@ -0,0 +1,331 @@
+
+
+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**](https://mmaction2.readthedocs.io/en/latest/supported_datasets.html) 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](/docs_zh_CN/benchmark.md)
+
+## 数据集准备
+
+请参考 [数据准备](/docs_zh_CN/data_preparation.md) 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](/docs_zh_CN/supported_datasets.md) 中
+
+## 常见问题
+
+请参考 [FAQ](/docs_zh_CN/faq.md) 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**. [\[论文\]](https://arxiv.org/abs/2107.10161)[\[代码\]](https://github.com/Cogito2012/DEAR)
+- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [\[论文\]](https://arxiv.org/abs/2103.17263)[\[代码\]](https://github.com/xvjiarui/VFS)
+- Video Swin Transformer. [\[论文\]](https://arxiv.org/abs/2106.13230)[\[代码\]](https://github.com/SwinTransformer/Video-Swin-Transformer)
+
+更多详情可见 [相关工作](docs/projects.md)
+
+## 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](/.github/CONTRIBUTING.md) 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：
+
+```BibTeX
+@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](/LICENSE)
+
+## OpenMMLab 的其他项目
+
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+
+## 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](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
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