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https://github.com/open-mmlab/mmaction

An open-source toolbox for action understanding based on PyTorch
https://github.com/open-mmlab/mmaction

action-detection action-recognition pytorch spatial-temporal-action-detection temporal-action-detection temporal-action-localization video-understanding

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An open-source toolbox for action understanding based on PyTorch

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README 

        
# MMAction



## Introduction

MMAction is an open source toolbox for action understanding based on PyTorch.

It is a part of the [open-mmlab](https://github.com/open-mmlab) project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).



### Major Features

- MMAction is capable of dealing with all of the tasks below.



  - action recognition from trimmed videos

  - temporal action detection (also known as action localization) in untrimmed videos

  - spatial-temporal action detection in untrimmed videos.



- Support for various datasets



  Video datasets have emerging throughout the recent years and have greatly fostered the devlopment of this field.

  MMAction provides tools to deal with various datasets.



- Support for multiple action understanding frameworks



  MMAction implements popular frameworks for action understanding:



  - For action recognition, various algorithms are implemented, including TSN, I3D, SlowFast, R(2+1)D, CSN.

  - For temporal action detection, we implement SSN.

  - For spatial temporal atomic action detection, a Fast-RCNN baseline is provided.



- Modular design



  The tasks in human action understanding share some common aspects such as backbones, and long-term and short-term sampling schemes.

  Also, tasks can benefit from each other. For example, a better backbone for action recognition will bring performance gain for action detection.

  Modular design enables us to view action understanding in a more integrated perspective.



## License

The project is release under the [Apache 2.0 license](https://github.com/open-mmlab/mmaction/blob/master/LICENSE).



## Updates

[OmniSource](https://arxiv.org/abs/2003.13042) Model Release (22/08/2020)

- We release several models of our work [OmniSource](https://arxiv.org/abs/2003.13042). These models are jointly trained with

Kinetics-400 and OmniSourced web dataset. Those models are of good performance (Top1 Accuracy: **75.7%** for 3-segment TSN and **80.4%** for SlowOnly on Kinetics-400 val) and the learned representation transfer well to other tasks.



v0.2.0 (15/03/2020)

- We build a diversified modelzoo for action recognition, which include popular algorithms (TSN, I3D, SlowFast, R(2+1)D, CSN). The performance is aligned with or better than the original papers.



v0.1.0 (19/06/2019)

- MMAction is online!



## Model zoo

Results and reference models are available in the [model zoo](https://github.com/open-mmlab/mmaction/blob/master/MODEL_ZOO.md).



## Installation

Please refer to [INSTALL.md](https://github.com/open-mmlab/mmaction/blob/master/INSTALL.md) for installation.



Update: for Docker installation, Please refer to [DOCKER.md](https://github.com/open-mmlab/mmaction/blob/master/DOCKER.md) for using docker for this project.



## Data preparation

Please refer to [DATASET.md](https://github.com/open-mmlab/mmaction/blob/master/DATASET.md) for a general knowledge of data preparation.

Detailed documents for the supported datasets are available in `data_tools/`.



## Get started

Please refer to [GETTING_STARTED.md](https://github.com/open-mmlab/mmaction/blob/master/GETTING_STARTED.md) for detailed examples and abstract usage.



## Contributing

We appreciate all contributions to improve MMAction.

Please refer to [CONTRUBUTING.md](https://github.com/open-mmlab/mmaction/blob/master/CONTRIBUTING.md) for the contributing guideline.



## Citation

If you use our codebase or models in your research, please cite this work.

We will release a technical report later.

```

@misc{mmaction2019,

  author =       {Yue Zhao, Yuanjun Xiong, Dahua Lin},

  title =        {MMAction},

  howpublished = {\url{https://github.com/open-mmlab/mmaction}},

  year =         {2019}

}

```



## Contact

If you have any question, please file an issue or contact the author:

```

Yue Zhao: [email protected]

```