mmaction2 1.0.0

OpenMMLab Video Understanding Toolbox and Benchmark

 

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📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🚀Ongoing Projects | 🤔Reporting Issues

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📄 Table of Contents

• 📄 Table of Contents
• 🥳 🚀 What's New
• 📖 Introduction
• 🎁 Major Features
• 🛠️ Installation
• 👀 Model Zoo
• 👨‍🏫 Get Started
• 🎫 License
• 🖊️ Citation
• 🙌 Contributing
• 🤝 Acknowledgement
• 🏗️ Projects in OpenMMLab

🥳 🚀 What's New 🔝

The default branch has been switched to 1.x from master, 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 (2023.04.06): v1.0.0 with the following new features:

• Support RGB-PoseC3D(CVPR'2022).
• Support training UniFormer V2(Arxiv'2022).
• Support MSG3D(CVPR'2020) and CTRGCN(CVPR'2021) in projects.
• Refactor and provide more user-friendly documentation.

📖 Introduction 🔝

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

🎁 Major Features 🔝

• 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 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.

• Well tested and documented: We provide detailed documentation and API reference, as well as unit tests.

🛠️ Installation 🔝

MMAction2 depends on PyTorch, MMCV, MMEngine, MMDetection (optional, for spatial-temporal detection tasks) and MMPose (optional, for skeleton based tasks).

Please refer to install.md for detailed instructions.

Quick instructions

conda create --name openmmlab python=3.8 -y
conda activate open-mmlab
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 'mmcv>=2.0.0'
mim install "mmdet>=3.0.0"  # optional
mim install "mmpose>=1.0.0"  # optional
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
git checkout 1.x
pip3 install -e .

👀 Model Zoo 🔝

Results and models are available in the model zoo.

Supported model

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)	VideoSwin (CVPR'2022)	VideoMAE (NeurIPS'2022)
MViT V2 (CVPR'2022)	UniFormer V1 (ICLR'2022)	UniFormer V2 (Arxiv'2022)
Action Localization
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 (CVPR'2022)	STGCN++ (ArXiv'2022)	CTRGCN (CVPR'2021)
MSG3D (CVPR'2020)

Supported dataset

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)
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)	AVA-Kinetics (Homepage) (Arxiv'2020)
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)

👨‍🏫 Get Started 🔝

For tutorials, we provide the following user guides for basic usage:

• Migration from MMAction2 0.X
• Learn about Configs
• Prepare Datasets
• Inference with Existing Models
• Training and Testing

Research works built on MMAction2 by users from community

• 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]

🎫 License 🔝

This project is released under the Apache 2.0 license.

🖊️ Citation 🔝

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}
}

🙌 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 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.

🏗️ Projects in OpenMMLab 🔝

• MMEngine: OpenMMLab foundational library for training deep learning models.
• 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.
• 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.