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The default branch has been switched to main
(previous 1.x
) from master
(current 0.x
), and we encourage users to migrate to the latest version with more supported models, stronger pre-training checkpoints and simpler coding. Please refer to Migration Guide for more details.
Release (2023.10.12): v1.2.0 with the following new features:
MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project.
Action Recognition on Kinetics-400 (left) and Skeleton-based Action Recognition on NTU-RGB+D-120 (right)
Skeleton-based Spatio-Temporal Action Detection and Action Recognition Results on Kinetics-400
Spatio-Temporal Action Detection Results on AVA-2.1
Modular design: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules.
Support five major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding tasks, including action recognition, action localization, spatio-temporal action detection, skeleton-based action detection and video retrieval.
Well tested and documented: We provide detailed documentation and API reference, as well as unit tests.
MMAction2 depends on PyTorch, MMCV, MMEngine, MMDetection (optional) and MMPose (optional).
Please refer to install.md for detailed instructions.
conda create --name openmmlab python=3.8 -y
conda activate openmmlab
conda install pytorch torchvision -c pytorch # This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they match your environment.
pip install -U openmim
mim install mmengine
mim install mmcv
mim install mmdet # optional
mim install mmpose # optional
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip install -v -e .
Results and models are available in the model zoo.
Action Recognition | ||||
C3D (CVPR'2014) | TSN (ECCV'2016) | I3D (CVPR'2017) | C2D (CVPR'2018) | 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) |
MultiModality: Audio (ArXiv'2020) | TANet (ArXiv'2020) | TimeSformer (ICML'2021) | ActionCLIP (ArXiv'2021) | VideoSwin (CVPR'2022) |
VideoMAE (NeurIPS'2022) | MViT V2 (CVPR'2022) | UniFormer V1 (ICLR'2022) | UniFormer V2 (Arxiv'2022) | VideoMAE V2 (CVPR'2023) |
Action Localization | ||||
BSN (ECCV'2018) | BMN (ICCV'2019) | TCANet (CVPR'2021) | ||
Spatio-Temporal Action Detection | ||||
ACRN (ECCV'2018) | SlowOnly+Fast R-CNN (ICCV'2019) | SlowFast+Fast R-CNN (ICCV'2019) | LFB (CVPR'2019) | VideoMAE (NeurIPS'2022) |
Skeleton-based Action Recognition | ||||
ST-GCN (AAAI'2018) | 2s-AGCN (CVPR'2019) | PoseC3D (CVPR'2022) | STGCN++ (ArXiv'2022) | CTRGCN (CVPR'2021) |
MSG3D (CVPR'2020) | ||||
Video Retrieval | ||||
CLIP4Clip (ArXiv'2022) |
Action Recognition | |||
HMDB51 (Homepage) (ICCV'2011) | UCF101 (Homepage) (CRCV-IR-12-01) | ActivityNet (Homepage) (CVPR'2015) | Kinetics-[400/600/700] (Homepage) (CVPR'2017) |
SthV1 (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) | Kinetics-710 (Homepage) (Arxiv'2022) | ||
Action Localization | |||
THUMOS14 (Homepage) (THUMOS Challenge 2014) | ActivityNet (Homepage) (CVPR'2015) | HACS (Homepage) (ICCV'2019) | |
Spatio-Temporal Action Detection | |||
UCF101-24* (Homepage) (CRCV-IR-12-01) | JHMDB* (Homepage) (ICCV'2015) | AVA (Homepage) (CVPR'2018) | AVA-Kinetics (Homepage) (Arxiv'2020) |
MultiSports (Homepage) (ICCV'2021) | |||
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) |
Video Retrieval | |||
MSRVTT (Homepage) (CVPR'2016) |
For tutorials, we provide the following user guides for basic usage:
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.
FAQs
OpenMMLab Video Understanding Toolbox and Benchmark
We found that mmaction2 demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago.ย It has 1 open source maintainer collaborating on the project.
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