martinjolif/football-player-detection

Football Game Object Detection Dataset

Dataset Description

This dataset consists of images captured from a football match in a stadium, taken from a camera positioned near the middle of the pitch. Each image contains annotations classifying objects into four categories:

1. Ball
2. Goalkeeper
3. Player
4. Referee

For every object in an image, a bounding box is provided, represented in the format:

• x_center: X-coordinate of the bounding box center
• y_center: Y-coordinate of the bounding box center
• width: Width of the bounding box
• height: Height of the bounding box

This dataset is suitable for tasks such as object detection, and can be used to detect players in football game images or videos.

Detecting players can be challenging, as they are often close together, can partially occlude each other, and can be confused with referees.

Usage

Download dataset in your current working directory cwd:

hf download martinjolif/football-player-detection --repo-type dataset --local-dir cwd

Finetune on this dataset:

yolo detect train data=data/data.yaml model=yolo11n.pt epochs=1 batch=32 imgsz=640 device=mps

Validate custom-trained model:

yolo detect val model=path/to/best.pt

References

The dataset comes from Roboflow Universe, where you can also visualize the bounding boxes drawn around each object in the images.

Cite the dataset:

@misc{
  football-players-detection-3zvbc-yyhdl_dataset,
  title = {football-players-detection Dataset},
  type = {Open Source Dataset},
  author = {Football project},
  howpublished = {\url{https://universe.roboflow.com/football-project-pifbc/football-players-detection-3zvbc-yyhdl}},
  url = {https://universe.roboflow.com/football-project-pifbc/football-players-detection-3zvbc-yyhdl},
  journal = {Roboflow Universe},
  publisher = {Roboflow},
  year = {2025},
  month = {oct},
  note = {visited on 2025-12-12},
}