Domain: Soccer video understanding
Task: Action Spotting
Modality: Video
Classes: [PASS, DRIVE, HEADER, HIGH PASS, OUT, CROSS, THROW IN, SHOT, BALL PLAYER BLOCK, PLAYER SUCCESSFUL TACKLE, FREE KICK, GOAL]

📊 Benchmark Results

Metric	Score
tight mAP	47.98
loose mAP	58.35

🔧 Using with OpenSportsLib

For more details about OpenSportsLib visit the below link

👉 Github - https://github.com/OpenSportsLab/opensportslib

👉 PyPi - https://pypi.org/project/opensportslib/

👉 Documentations - https://opensportslab.github.io/opensportslib/

Import the library

python

import opensportslib
print("OpenSportsLib imported successfully")

Run inference

python

from opensportslib.apis import LocalizationModel

my_model = LocalizationModel(
    config="/path/to/localization.yaml",
👉   weights="OpenSportsLab/OSL-loc-snbas-2025-e2e",
)

predictions = my_model.infer(
    test_set="/path/to/test.json",
)

saved_predictions = my_model.save_predictions(
    output_path="/path/to/predictions.json",
    predictions=predictions,
)

metrics = my_model.evaluate(
    test_set="/path/to/test.json",
    predictions=saved_predictions,
)

print(metrics)

📜 License

Open source license: AGPL 3.0 for research, academic, and community use.
Commercial license: For proprietary or commercial deployment, please contact the project maintainers.

📎 Citation

@misc{opensportslib_e2e_localization_snbas_2025,
  title={OpenSportsLib Localization E2E SNBAS 2025},
  author={OpenSportsLab},
  year={2026},
  howpublished={https://huggingface.co/OpenSportsLab/oslib-e2e-localization-snbas-2025}
}

🙏 Acknowledgements

Dataset: SoccerNet / OpenSportsLab
Library: https://github.com/OpenSportsLab/opensportslib