/OpenTAD

OpenTAD is an open-source temporal action detection (TAD) toolbox based on PyTorch.

Primary LanguagePythonApache License 2.0Apache-2.0

OpenTAD: An Open-Source Temporal Action Detection Toolbox.

OpenTAD is an open-source temporal action detection (TAD) toolbox based on PyTorch.

๐Ÿฅณ What's New

๐Ÿ“– 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.

๐ŸŒŸ Model Zoo

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.

๐Ÿ› ๏ธ Installation

Please refer to install.md for installation.

๐Ÿ“ Data Preparation

Please refer to data.md for data preparation.

๐Ÿš€ Usage

Please refer to usage.md for details of training and evaluation scripts.

๐Ÿ“„ Updates

Please refer to changelog.md for update details.

๐Ÿค Roadmap

All the things that need to be done in the future is in roadmap.md.

๐Ÿ–Š๏ธ 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 Leรณn Alcรกzar, 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.