gst-python-ml football models
Weights for the football broadcast demo in 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, ball labels only
- martinjolif/football-ball-detection
- 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.
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