OpenSportsLab/OSL-VQA-XFOUL-XVARS-lora

OpenSportsLib VQA Model (X-VARS VideoChatGPT 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: X-VARS VideoChatGPT + LoRA adapter
• Backend: xvars_videochatgpt_lora
• Library: OpenSportsLib
• Input: Soccer video clips plus natural-language questions
• Feature path: X-VARS-compatible CLIP features with referee priors

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
72.24%	50.00%

Using with OpenSportsLib

For more details about OpenSportsLib:

• GitHub: https://github.com/OpenSportsLab/opensportslib
• PyPI: https://pypi.org/project/opensportslib/
• Documentation: https://opensportslab.github.io/opensportslib/

Import the library

import opensportslib
print("OpenSportsLib imported successfully")

Run inference

from opensportslib.apis import VQAModel

my_model = VQAModel(
    config="opensportslib/configs/vqa/xvars.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 X-VARS VQA path driven by opensportslib/configs/vqa/xvars.yaml.
• The original training setup used X-VARS-compatible visual features, referee priors, and the VideoChatGPT-style multimodal interface.

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_xvars_vqa_xfoul_lora_2026,
  title={OpenSportsLib X-VARS VideoChatGPT LoRA for Soccer VQA},
  author={OpenSportsLab},
  year={2026},
  howpublished={https://huggingface.co/OpenSportsLab}
}

Acknowledgements

• Dataset: OpenSportsLab / OSL-XFoul
• Library: https://github.com/OpenSportsLab/opensportslib
• Model pipeline: OpenSportsLib X-VARS VQA backend