Introduction

English | 简体中文

MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.3+.

Action Recognition Results on Kinetics-400

Spatio-Temporal Action Detection Results on AVA-2.1

Skeleton-base Action Recognition Results on NTU-RGB+D-120

Major Features

• Modular design

We decompose the video understanding framework into different components and one can easily construct a customized video understanding framework by combining different modules.

• Support for various datasets

The toolbox directly supports multiple datasets, UCF101, Kinetics-[400/600/700], Something-Something V1&V2, Moments in Time, Multi-Moments in Time, THUMOS14, etc.

• Support for multiple video understanding frameworks

MMAction2 implements popular frameworks for video understanding:

• For action recognition, various algorithms are implemented, including TSN, TSM, TIN, R(2+1)D, I3D, SlowOnly, SlowFast, CSN, Non-local, etc.

• For temporal action localization, we implement BSN, BMN, SSN.

• For spatial temporal detection, we implement SlowOnly, SlowFast.

  • Well tested and documented

We provide detailed documentation and API reference, as well as unittests.

Changelog

v0.18.0 was released in 02/09/2021. Please refer to changelog.md for details and release history.

Benchmark

| 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 |

Details can be found in benchmark.

ModelZoo

Supported methods for Action Recognition:

Supported methods for Temporal Action Detection:

Supported methods for Spatial Temporal Action Detection:

Supported methods for Skeleton-based Action Recognition:

Results and models are available in the README.md of each method's config directory. A summary can be found in the model zoo page.

We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in Issues.

Dataset

Supported datasets:

Supported datasets for Action Recognition:

Supported datasets for Temporal Action Detection

Supported datasets for Spatial Temporal Action Detection

Supported datasets for Skeleton-based Action Detection

Datasets marked with 🔲 are not fully supported yet, but related dataset preparation steps are provided.

Installation

Please refer to install.md for installation.

Data Preparation

Please refer to data_preparation.md for a general knowledge of data preparation. The supported datasets are listed in supported_datasets.md

Get Started

Please see getting_started.md for the basic usage of MMAction2. There are also tutorials:

• learn about configs
• finetuning models
• adding new dataset
• designing data pipeline
• adding new modules
• exporting model to onnx
• customizing runtime settings

A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.

FAQ

Please refer to FAQ for frequently asked questions.

License

This project is released under the Apache 2.0 license.

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} }

Contributing

We appreciate all contributions to improve MMAction2. Please refer to 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, as well as users who give valuable feedbacks. 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 own new models.

Projects in OpenMMLab

• MMCV: OpenMMLab foundational library for computer vision.
• MIM: MIM Installs OpenMMLab Packages.
• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation video understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
• MMGeneration: OpenMMLab image and video generative models toolbox.