OpenSportsLib VQA Model (Qwen3-VL LoRA)

Overview

This model is a VQA LoRA adapter produced with OpenSportsLib for soccer foul understanding and referee-style visual question answering.

  • Task: Visual Question Answering (VQA)
  • Architecture: Qwen3-VL + LoRA adapter
  • Backend: qwen_vl_native_lora
  • Base model: Qwen/Qwen3-VL-8B-Instruct
  • Library: OpenSportsLib
  • Input: Soccer video clips plus natural-language questions
  • Visual path: Native end-to-end QwenVL video understanding

Dataset

Training Dataset

This adapter was trained on the OpenSportsLib XFoul VQA setup built on the OSL-XFoul dataset.

  • Dataset name: OSL-XFoul
  • Domain: Soccer video understanding and officiating
  • Task: Visual question answering
  • Modality: Video + text
  • Training samples: 16,568
  • Validation samples: 2,219

Benchmark Results

Accuracy Balanced Accuracy
69.96% 48.33%

Using with OpenSportsLib

For more details about OpenSportsLib:

Run inference

from opensportslib.apis import VQAModel

my_model = VQAModel(
    config="opensportslib/configs/vqa/qwen3_vl_native.yaml",
    weights="YOUR_HF_REPO_ID",
)

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

single_prediction = my_model.infer(
    video_path="/path/to/video.mp4",
    question="Was this a foul? What card should be given?",
)

print(predictions)
print(single_prediction)

Notes

  • This repository stores a PEFT LoRA adapter, not a merged standalone base model.
  • The adapter is intended for the OpenSportsLib native QwenVL VQA path driven by opensportslib/configs/vqa/qwen3_vl_native.yaml.
  • The original training setup used frame-based native multimodal inputs with Qwen/Qwen3-VL-8B-Instruct.

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_qwen3_vl_vqa_xfoul_lora_2026,
  title={OpenSportsLib Qwen3-VL LoRA for Soccer VQA},
  author={OpenSportsLab},
  year={2026},
  howpublished={https://huggingface.co/OpenSportsLab}
}

Acknowledgements

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