| --- |
| license: agpl-3.0 |
| tags: |
| - object-detection |
| - yolo |
| - ultralytics |
| - football |
| --- |
| |
| # gst-python-ml football models |
|
|
| Weights for the football broadcast demo in [gst-python-ml](https://github.com/collabora/gst-python-ml). `demo/football/fetch_models.py` downloads them into `models/football/`. |
|
|
| | file | classes | input | role | |
| |---|---|---|---| |
| | `football.pt`, `football.onnx`, `football_fp16.onnx`, `football_int8.onnx` | ball, goalkeeper, player, referee | 640 | player detector | |
| | `ball11n.pt` | ball | 1280 | ball detector, run after the player detector | |
|
|
| ## ball11n.pt |
|
|
| yolo11n fine-tuned from the ultralytics COCO weights for 40 epochs at imgsz 1280 on a one-class ball dataset of 15,357 training images. The player detector's own ball class is dropped in the demo in favour of this model. |
|
|
| | set | imgsz | P | R | mAP50 | mAP50-95 | |
| |---|---|---|---|---|---| |
| | SoccerNet v3 H250 test | 1280 | 0.774 | 0.708 | 0.701 | 0.327 | |
| | football-player-detection valid+test, ball only | 1280 | 0.921 | 0.850 | 0.888 | 0.407 | |
|
|
| Training data, all CC BY 4.0: |
|
|
| - SoccerNet v3 H250, Zenodo record [7808511](https://zenodo.org/records/7808511), ball labels only |
| - [martinjolif/football-ball-detection](https://huggingface.co/datasets/martinjolif/football-ball-detection) |
| - [martinjolif/football-player-detection](https://huggingface.co/datasets/martinjolif/football-player-detection), held out as the second evaluation set |
|
|
| ## Licence |
|
|
| The weights derive from ultralytics YOLO and are released under AGPL-3.0 like the ultralytics package. |
|
|
