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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 1.5+.
Action Recognition Results on Kinetics-400
Skeleton-based Action Recognition Results on NTU-RGB+D-120
Skeleton-based Spatio-Temporal Action Detection and Action Recognition Results on Kinetics-400
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.
- (2022-03-04) We support Multigrid on Kinetics400, achieve 76.07% Top-1 accuracy and accelerate training speed.
- (2021-11-24) We support 2s-AGCN on NTU60 XSub, achieve 86.06% Top-1 accuracy on joint stream and 86.89% Top-1 accuracy on bone stream respectively.
- (2021-10-29) We provide a demo for skeleton-based and rgb-based spatio-temporal detection and action recognition (demo/demo_video_structuralize.py).
- (2021-10-26) We train and test ST-GCN on NTU60 with 3D keypoint annotations, achieve 84.61% Top-1 accuracy (higher than 81.5% in the paper).
- (2021-10-25) We provide a script(tools/data/skeleton/gen_ntu_rgbd_raw.py) to convert the NTU60 and NTU120 3D raw skeleton data to our format.
- (2021-10-25) We provide a guide on how to train PoseC3D with custom datasets, bit-scientist authored this PR!
- (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.
Release: v0.24.0 was released in 05/05/2022. Please refer to changelog.md for details and release history.
MMAction2 depends on PyTorch, MMCV, MMDetection (optional), and MMPose(optional). Below are quick steps for installation. Please refer to install.md for more detailed instruction.
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet # optional
mim install mmpose # optional
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip3 install -e .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) | 2s-AGCN (CVPR'2019) | 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.
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.
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}
}This project is released under the Apache 2.0 license.
- 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.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.