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OpenTAD: An Open-Source Temporal Action Detection Toolbox.
<p align="left"> <!-- <a href="https://arxiv.org/abs/xxx.xxx" alt="arXiv"> --> <!-- <img src="https://img.shields.io/badge/arXiv-xxx.xxx-b31b1b.svg?style=flat" /></a> --> <a href="https://github.com/sming256/opentad/blob/main/LICENSE" alt="license"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" /></a> <a href="https://github.com/sming256/OpenTAD/issues" alt="docs"> <img src="https://img.shields.io/github/issues-raw/sming256/OpenTAD?color=%23FF9600" /></a> <a href="https://img.shields.io/github/stars/sming256/opentad" alt="arXiv"> <img src="https://img.shields.io/github/stars/sming256/opentad" /></a> </p>OpenTAD is an open-source temporal action detection (TAD) toolbox based on PyTorch.
🥳 What's New
- A technical report of this library will be provided soon.
- [2024/07/25] 🔥 We rank 1st in the Action Recognition, Action Detection, and Audio-Based Interaction Detection tasks of the EPIC-KITCHENS-100 2024 Challenge, as well as 1st place in the Moment Queries task of the Ego4D 2024 Challenge! Code is released at CausalTAD (arxiv'24).
- [2024/07/07] 🔥 We support DyFADet (ECCV'24). Thanks to the authors's effort!
- [2024/06/14] We release version v0.3, which brings many new features and improvements.
- [2024/04/17] We release the AdaTAD (CVPR'24), which can achieve average mAP of 42.90% on ActivityNet and 77.07% on THUMOS14.
📖 Major Features
- Support SoTA TAD methods with modular design. We decompose the TAD pipeline into different components, and implement them in a modular way. This design makes it easy to implement new methods and reproduce existing methods.
- Support multiple TAD datasets. We support 9 TAD datasets, including ActivityNet-1.3, THUMOS-14, HACS, Ego4D-MQ, EPIC-Kitchens-100, FineAction, Multi-THUMOS, Charades, and EPIC-Sounds Detection datasets.
- Support feature-based training and end-to-end training. The feature-based training can easily be extended to end-to-end training with raw video input, and the video backbone can be easily replaced.
- Release various pre-extracted features. We release the feature extraction code, as well as many pre-extracted features on each dataset.
🌟 Model Zoo
<table align="center"> <tbody> <tr align="center" valign="bottom"> <td> <b>One Stage</b> </td> <td> <b>Two Stage</b> </td> <td> <b>DETR</b> </td> <td> <b>End-to-End Training</b> </td> </tr> <tr valign="top"> <td> <ul> <li><a href="configs/actionformer">ActionFormer (ECCV'22)</a></li> <li><a href="configs/tridet">TriDet (CVPR'23)</a></li> <li><a href="configs/temporalmaxer">TemporalMaxer (arXiv'23)</a></li> <li><a href="configs/videomambasuite">VideoMambaSuite (arXiv'24)</a></li> <li><a href="configs/dyfadet">DyFADet (ECCV'24)</a></li> <li><a href="configs/causaltad">CausalTAD (arXiv'24)</a></li> </ul> </td> <td> <ul> <li><a href="configs/bmn">BMN (ICCV'19)</a></li> <li><a href="configs/gtad">GTAD (CVPR'20)</a></li> <li><a href="configs/tsi">TSI (ACCV'20)</a></li> <li><a href="configs/vsgn">VSGN (ICCV'21)</a></li> </ul> </td> <td> <ul> <li><a href="configs/tadtr">TadTR (TIP'22)</a></li> </ul> </td> <td> <ul> <li><a href="configs/afsd">AFSD (CVPR'21)</a></li> <li><a href="configs/tadtr">E2E-TAD (CVPR'22)</a></li> <li><a href="configs/etad">ETAD (CVPRW'23)</a></li> <li><a href="configs/re2tal">Re2TAL (CVPR'23)</a></li> <li><a href="configs/adatad">AdaTAD (CVPR'24)</a></li> </ul> </td> </tr> </td> </tr> </tbody> </table>The detailed configs, results, and pretrained models of each method can be found in above folders.
🛠️ Installation
Please refer to install.md for installation and data preparation.
🚀 Usage
Please refer to usage.md for details of training and evaluation scripts.
📄 Updates
Please refer to changelog.md for update details.
🤝 Roadmap
All the things that need to be done in the future is in roadmap.md.
🖊️ Citation
[Acknowledgement] This repo is inspired by OpenMMLab project, and we give our thanks to their contributors.
If you think this repo is helpful, please cite us:
@misc{2024opentad,
title={OpenTAD: An Open-Source Toolbox for Temporal Action Detection},
author={Shuming Liu, Chen Zhao, Fatimah Zohra, Mattia Soldan, Carlos Hinojosa, Alejandro Pardo, Anthony Cioppa, Lama Alssum, Mengmeng Xu, Merey Ramazanova, Juan León Alcázar, Silvio Giancola, Bernard Ghanem},
howpublished = {\url{https://github.com/sming256/opentad}},
year={2024}
}
If you have any questions, please contact: shuming.liu@kaust.edu.sa.