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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
Spatio-Temporal Action Detection Results on AVA-2.1
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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.
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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.
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Support for multiple video understanding frameworks
MMAction2 implements popular frameworks for video understanding:
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For action recognition, various algorithms are implemented, including TSN, TSM, TIN, R(2+1)D, I3D, SlowOnly, SlowFast, CSN, Non-local, etc.
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For temporal action localization, we implement BSN, BMN, SSN.
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For spatial temporal detection, we implement SlowOnly, SlowFast.
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Well tested and documented
We provide detailed documentation and API reference, as well as unittests.
v0.13.0 was released in 31/03/2021. Please refer to changelog.md for details and release history.
| 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.
Supported methods for Action Recognition:
(click to collapse)
- ✅ TSN (ECCV'2016)
- ✅ TSM (ICCV'2019)
- ✅ TSM Non-Local (ICCV'2019)
- ✅ R(2+1)D (CVPR'2018)
- ✅ I3D (CVPR'2017)
- ✅ I3D Non-Local (CVPR'2018)
- ✅ SlowOnly (ICCV'2019)
- ✅ SlowFast (ICCV'2019)
- ✅ CSN (ICCV'2019)
- ✅ TIN (AAAI'2020)
- ✅ TPN (CVPR'2020)
- ✅ C3D (CVPR'2014)
- ✅ X3D (CVPR'2020)
- ✅ OmniSource (ECCV'2020)
- ✅ MultiModality: Audio (ArXiv'2020)
- ✅ TANet (ArXiv'2020)
- ✅ TRN (CVPR'2015)
Supported methods for Temporal Action Detection:
Supported methods for Spatial Temporal Action Detection:
(click to collapse)
- ✅ SlowOnly+Fast R-CNN (ICCV'2019)
- ✅ SlowFast+Fast R-CNN (ICCV'2019)
- ✅ Long-Term Feature Bank (CVPR'2019)
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.
Supported datasets:
Supported datasets for Action Recognition:
(click to collapse)
- ✅ UCF101 [ Homepage ] (CRCV-IR-12-01)
- ✅ HMDB51 [ Homepage ] (ICCV'2011)
- ✅ Kinetics-[400/600/700] [ Homepage ] (CVPR'2017)
- ✅ Something-Something V1 [ Homepage ] (ICCV'2017)
- ✅ Something-Something V2 [ Homepage ] (ICCV'2017)
- ✅ Moments in Time [ Homepage ] (TPAMI'2019)
- ✅ Multi-Moments in Time [ Homepage ] (ArXiv'2019)
- ✅ HVU [ Homepage ] (ECCV'2020)
- ✅ Jester [ Homepage ] (ICCV'2019)
- ✅ GYM [ Homepage ] (CVPR'2020)
- ✅ ActivityNet [ Homepage ] (CVPR'2015)
Supported datasets for Temporal Action Detection
(click to collapse)
- ✅ ActivityNet [ Homepage ] (CVPR'2015)
- ✅ THUMOS14 [ Homepage ] (THUMOS Challenge 2014)
Supported datasets for Spatial Temporal Action Detection
(click to collapse)
Datasets marked with 🔲 are not fully supported yet, but related dataset preparation steps are provided.
Please refer to install.md for installation.
Please refer to data_preparation.md for a general knowledge of data preparation. The supported datasets are listed in supported_datasets.md
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.
Please refer to FAQ for frequently asked questions.
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, 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.
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