---
annotations_creators: []
language: en
license: mit
size_categories:
- 1K<n<10K
task_categories:
- object-detection
task_ids: []
pretty_name: SoccerNet-V3
tags:
- fiftyone
- group
- object-detection
- sports
- tracking
- action-spotting
- game-state-recognition
dataset_summary: >



  ![image/png](dataset_preview.jpg)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1799
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  import fiftyone.utils.huggingface as fouh


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset = fouh.load_from_hub("Voxel51/SoccerNet-V3")


  # Launch the App

  session = fo.launch_app(dataset)

  ```
---

# Dataset Card for SoccerNet-V3

SoccerNet is a large-scale dataset for soccer video understanding. It has evolved over the years to include various tasks such as action spotting,
camera calibration, player re-identification and tracking. It is composed of 550 complete broadcast soccer games and 12 single camera games
taken from the major European leagues. SoccerNet is not only dataset, but also yearly challenges where the best teams compete at the international level. 




![image/png](dataset_preview.jpg)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1799 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/SoccerNet-V3")

# Launch the App
session = fo.launch_app(dataset)
```


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->


- **Language(s) (NLP):** en
- **License:** mit

### Dataset Sources
<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/SoccerNet
- **Paper** [SoccerNet 2023 Challenges Results](https://arxiv.org/abs/2309.06006)
- **Demo:** https://try.fiftyone.ai/datasets/soccernet-v3/samples
- **Homepage** https://www.soccer-net.org/


## Dataset Creation

Dataset Authors:

Copyright (c) 2021 holders: 

- University of Liège (ULiège), Belgium.
- King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
- Marc Van Droogenbroeck (M.VanDroogenbroeck@uliege.be), Professor at the University of Liège (ULiège).

Code Contributing Authors: 

- Anthony Cioppa (anthony.cioppa@uliege.be), University of Liège (ULiège), Montefiore Institute, TELIM.
- Adrien Deliège (adrien.deliege@uliege.be), University of Liège (ULiège), Montefiore Institute, TELIM.
- Silvio Giancola (silvio.giancola@kaust.edu.sa), King Abdullah University of Science and Technology (KAUST), Image and Video Understanding Laboratory (IVUL), part of the Visual Computing Center (VCC).

Supervision from:

- Bernard Ghanem, King Abdullah University of Science and Technology (KAUST).
- Marc Van Droogenbroeck, University of Liège (ULiège).

### Funding

Anthony Cioppa is funded by the FRIA, Belgium.
This work is supported by the DeepSport and TRAIL projects of the Walloon Region, at the University of Liège (ULiège), Belgium.
This work was supported by the Service Public de Wallonie (SPW) Recherche under the DeepSport project and Grant No.326 2010235 (ARIAC by https://DigitalWallonia4.ai)
This work is also supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) (award327 OSR-CRG2017-3405).


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex

@inproceedings{Giancola_2018,
   title={SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos},
   url={http://dx.doi.org/10.1109/CVPRW.2018.00223},
   DOI={10.1109/cvprw.2018.00223},
   booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
   publisher={IEEE},
   author={Giancola, Silvio and Amine, Mohieddine and Dghaily, Tarek and Ghanem, Bernard},
   year={2018},
   month=jun }

@misc{deliège2021soccernetv2,
      title={SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos}, 
      author={Adrien Deliège and Anthony Cioppa and Silvio Giancola and Meisam J. Seikavandi and Jacob V. Dueholm and Kamal Nasrollahi and Bernard Ghanem and Thomas B. Moeslund and Marc Van Droogenbroeck},
      year={2021},
      eprint={2011.13367},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{cioppa2022soccernettracking,
      title={SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos}, 
      author={Anthony Cioppa and Silvio Giancola and Adrien Deliege and Le Kang and Xin Zhou and Zhiyu Cheng and Bernard Ghanem and Marc Van Droogenbroeck},
      year={2022},
      eprint={2204.06918},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{Cioppa2022,
  title={Scaling up SoccerNet with multi-view spatial localization and re-identification},
  author={Cioppa, Anthony and Deli{\`e}ge, Adrien and Giancola, Silvio and Ghanem, Bernard and Van Droogenbroeck, Marc},
  journal={Scientific Data},
  year={2022},
  volume={9},
  number={1},
  pages={355},
}
```



## Dataset Card Authors

[Jacob Marks](https://huggingface.co/jamarks)