OpenSportsLab/OSL-loc-snbas-2023-e2e

⚽ OpenSportsLib Localization Model (E2E)

📌 Overview

This model is a video-based localization model built using the OpenSportsLib, designed for soccer action localization.

• Task: Action Spotting / Localization
• Architecture: E2E
• Backend: Dali
• Library: OpenSportsLib
• Input: Video clips (224p)

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📂 Dataset

Training Dataset

This model is trained on the SoccerNet – Ball Action Spotting 2023 (2 classes):

👉 https://huggingface.co/datasets/OpenSportsLab/soccernetpro-localization-snbas/tree/224p-2023

• Domain: Soccer video understanding
• Task: Action Spotting
• Modality: Video
• Classes: [PASS, DRIVE]

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📊 Benchmark Results

Metric	Score
tight mAP	71.48
loose mAP	85.62

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🔧 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

import opensportslib
print("OpenSportsLib imported successfully")

Run inference

from opensportslib.apis import LocalizationModel

my_model = LocalizationModel(
    config="/path/to/localization.yaml",
👉   weights="OpenSportsLab/OSL-loc-snbas-2023-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)

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📜 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.

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📎 Citation

@misc{opensportslib_e2e_localization_snbas_2023,
  title={OpenSportsLib Localization E2E SNBAS 2023},
  author={OpenSportsLab},
  year={2026},
  howpublished={https://huggingface.co/OpenSportsLab/oslib-e2e-localization-snbas-2023}
}

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🙏 Acknowledgements

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