OpenTAD is an open-source temporal action detection (TAD) toolbox based on PyTorch.
- A technical report of this library will be provided soon.
- 2024/03/28: The beta version v0.1.0 of OpenTAD is released. Any feedbacks and suggestions are welcome!
- 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 8 TAD datasets, including ActivityNet-1.3, THUMOS-14, HACS, Ego4D-MQ, Epic-Kitchens-100, FineAction, Multi-THUMOS, Charades 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.
| One Stage | Two Stage | DETR | End-to-End Training |
The detailed configs, results, and pretrained models of each method can be found in above folders.
Please refer to install.md for installation and data preparation.
Please refer to usage.md for details of training and evaluation scripts.
Please refer to changelog.md for update details.
All the things that need to be done in the future is in roadmap.md.
[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.