# OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos
[](https://arxiv.org/abs/xxx.xxx)
[](https://github.com/SoccerNet/sn-spotting-pip/blob/main/LICENSE)
OSL-ActionSpotting is a plug-and-play library that unifies action
spotting algorithms.
## 🥳 What's New
- A technical report of this library will be provided soon.
## 📖 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 datasets.** We support new datasets by giving a intermediate JSON format.
- **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.
## 🌟 Model Zoo
| Feature based | End to end |
|:-------------:|:----------:|
| [AvgPool](https://arxiv.org/pdf/1804.04527.pdf) | [E2E-Spot](https://arxiv.org/pdf/2207.10213.pdf) |
| [MaxPool](https://arxiv.org/pdf/1804.04527.pdf) | |
| [NetVLAD](https://arxiv.org/pdf/1804.04527.pdf) | |
| [NetRVLAD](https://arxiv.org/pdf/1804.04527.pdf) | |
| [CALF](https://arxiv.org/pdf/1912.01326.pdf) | |
| [AvgPool++](https://arxiv.org/pdf/2104.06779.pdf) | |
| [MaxPool++](https://arxiv.org/pdf/2104.06779.pdf) | |
| [NetVLAD++](https://arxiv.org/pdf/2104.06779.pdf) | |
| [NetRVLAD++](https://arxiv.org/pdf/2104.06779.pdf)| |
## 🛠️ Installation
Please refer to [install.md](docs/install.md) for installation and data preparation.
## 🚀 Usage
Please refer to [usage.md](docs/usage.md) for details of training and evaluation scripts.
## 🤝 Roadmap
All the things that need to be done in the future is in [roadmap.md](docs/en/roadmap.md).
## 🖊️ Citation
If you think this repo is helpful, please cite us:
```bibtex
@misc{name,
title={},
author={},
howpublished = {\url{https://github.com/OpenSportsLab/OSL-ActionSpotting}},
year={2024}
}
```
If you have any questions, please contact: `yassine.benzakour@student.uliege.be`.
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