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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
Skeleton-base Action Recognition Results on NTU-RGB+D-120
Spatio-Temporal Action Detection Results on AVA-2.1
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Modular design: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules.
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Support four major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding tasks, including action recognition, action localization, spatio-temporal action detection, and skeleton-based action detection. We support 27 different algorithms and 20 different datasets for the four major tasks.
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Well tested and documented: We provide detailed documentation and API reference, as well as unit tests.
- (2021-10-16) We support PoseC3D on UCF101 and HMDB51, achieves 87.0% and 69.3% Top-1 accuracy with 2D skeletons only. Pre-extracted 2D skeletons are also available.
- (2021-10-12) We support TorchServe! Now recognition models in MMAction2 can be packed as a
.marfile and served with TorchServe. - (2021-09-11) We support ST-GCN, a well-known GCN-based approach for skeleton-based action recognition!
Release: v0.19.0 was released in 07/10/2021. Please refer to changelog.md for details and release history.
Please refer to install.md for installation.
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.
| Action Recognition | ||||
| 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) | ||
| Action Localization | ||||
| SSN (ICCV'2017) | BSN (ECCV'2018) | BMN (ICCV'2019) | ||
| Spatio-Temporal Action Detection | ||||
| ACRN (ECCV'2018) | SlowOnly+Fast R-CNN (ICCV'2019) | SlowFast+Fast R-CNN (ICCV'2019) | LFB (CVPR'2019) | |
| Skeleton-based Action Recognition | ||||
| ST-GCN (AAAI'2018) | PoseC3D (ArXiv'2021) | |||
Results and models are available in the README.md of each method's config directory. A summary can be found on 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.
| Action Recognition | |||
| HMDB51 (Homepage) (ICCV'2011) | UCF101 (Homepage) (CRCV-IR-12-01) | ActivityNet (Homepage) (CVPR'2015) | Kinetics-[400/600/700] (Homepage) (CVPR'2017) |
| SthV1 (Homepage) (ICCV'2017) | SthV2 (Homepage) (ICCV'2017) | Diving48 (Homepage) (ECCV'2018) | Jester (Homepage) (ICCV'2019) |
| Moments in Time (Homepage) (TPAMI'2019) | Multi-Moments in Time (Homepage) (ArXiv'2019) | HVU (Homepage) (ECCV'2020) | OmniSource (Homepage) (ECCV'2020) |
| FineGYM (Homepage) (CVPR'2020) | |||
| Action Localization | |||
| THUMOS14 (Homepage) (THUMOS Challenge 2014) | ActivityNet (Homepage) (CVPR'2015) | ||
| Spatio-Temporal Action Detection | |||
| UCF101-24* (Homepage) (CRCV-IR-12-01) | JHMDB* (Homepage) (ICCV'2015) | AVA (Homepage) (CVPR'2018) | |
| Skeleton-based Action Recognition | |||
| PoseC3D-FineGYM (Homepage) (ArXiv'2021) | PoseC3D-NTURGB+D (Homepage) (ArXiv'2021) | PoseC3D-UCF101 (Homepage) (ArXiv'2021) | PoseC3D-HMDB51 (Homepage) (ArXiv'2021) |
Datasets marked with * are not fully supported yet, but related dataset preparation steps are provided. A summary can be found on the Supported Datasets page.
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.
Please refer to data_preparation.md for a general knowledge of data preparation. The supported datasets are listed in supported_datasets.md
Please refer to FAQ for frequently asked questions.
Currently, there are many research works and projects built on MMAction2 by users from community, such as:
- Video Swin Transformer. [paper][github]
- Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 Oral. [paper][github]
- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 Oral. [paper][github]
etc., check projects.md to see all related projects.
This project is released under the Apache 2.0 license.
If you find this project useful in your research, please consider cite:
@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}
}We appreciate all contributions to improve MMAction2. Please refer to CONTRIBUTING.md in MMCV for more details about the contributing guideline.
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.
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