File size: 2,435 Bytes
7fdac42 9447489 7fdac42 fdf57c0 af0b227 fdf57c0 7fdac42 fdf57c0 7fdac42 fdf57c0 7fdac42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | ---
license: agpl-3.0
tags:
- soccer
- video
- spotting
- localization
---
# ⚽ 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)
---
## 📂 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]
---
## 📊 Benchmark Results
| Metric | Score |
|--------------|------|
| tight mAP | 71.48 |
| loose mAP | 85.62 |
---
## 🔧 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-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)
```
---
## 📜 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_2023,
title={OpenSportsLib Localization E2E SNBAS 2023},
author={OpenSportsLab},
year={2026},
howpublished={https://huggingface.co/OpenSportsLab/oslib-e2e-localization-snbas-2023}
}
```
---
## 🙏 Acknowledgements
- **Dataset**: SoccerNet / OpenSportsLab
- **Library**: https://github.com/OpenSportsLab/opensportslib |