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Soccer Qa 4b

⚠️ RESEARCH USE ONLY - NON-COMMERCIAL LICENSE

TipoText modelVersãoFP32Cnota

Fonte do modelo

Tipo
Text model
Tamanho do arquivo
17,2 GB
Versão
vFP32
Atualizado
30 de out. de 2026
Downloads
17
Fonte
Hugging Face

Descrição da fonte

Soccer QA 4B - Soccer Video Question Answering Model

⚠️ RESEARCH USE ONLY - NON-COMMERCIAL LICENSE

Soccer QA 4B is a unified video question-answering model specifically designed for soccer video understanding.

Model Description

This model can answer questions about soccer videos by analyzing visual content and generating natural language responses.

Example:

  • Input: Video + "What unfolded during the game in the video?"
  • Output: "During the game, there was a foul committed by a player from the yellow-jerseyed team, leading to a yellow card being issued..."

Architecture

  • Vision Encoder: DWT-VJEPA2-based video encoder (vit_giant, 1408 dim)
  • Text Model: LLaMA 3.2-3B with LoRA fine-tuning
  • Vision-Text Bridge: Learned projection layer (1408 → 2048 → 3072)
  • Specialization: Fine-tuned on soccer video QA data

Usage (Helper functions are in repo)

from soccer_qa_inference import SoccerQA

model = SoccerQA("/path/to/model")
answer = model.ask("video.mp4", "Was this a Foul?", max_tokens=45)
print(answer)

Model Details

  • Parameters: ~4B total
  • Input: Video files (16 frames, 256x256) + text questions
  • Output: Natural language answers
  • Domain: Soccer/football video analysis
  • Context: Handles complex game situations, player actions, fouls, etc.

Training Data

  • Soccer video clips with question-answer pairs
  • Covers various game situations: fouls, shots, saves, player actions
  • Annotated with detailed descriptions of game events

Limitations

  • Research use only, no commercial applications
  • Optimized specifically for soccer content
  • May not generalize well to other sports or video domains
  • Requires high-quality video input for best results

License

CC-BY-NC-4.0 - Research use only, no commercial applications permitted.

Citation

@misc{soccer-qa-4b-2025,
  title={Soccer QA 4B: Video Question Answering for Soccer Analysis},
  author={Varun Kodathala, Sports Vision},
  year={2025},
  note={Research model for soccer video understanding}
}

Fontes

1 fonte
Hugging FacePrimárioVersão e0ef6b8b795fb91c0998c089ab2dc5c291b1ce05
huggingface.co/sportsvision/soccer-qa-4b/tree/e0ef6b8b795fb91c0998c089ab2dc5c291b1ce05

Verificado 30 de out.

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Artefatos de modelo

1 artefato

model.safetensors

safetensors · FP32 (tensor header) · 17,2 GB · SHA-256 f268918412af…b7cc · Hugging Face

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Trechos de fonte

3 trechos

--- language: en license: cc-by-nc-4.0 tags: - soccer - video-qa - question-answering - vision-language - multimodal - sports-analysis library_name: transformers pipeline_tag: video-text-to-text --- # Soccer QA 4B - Soccer Video Question Answering Model **⚠️ RESEARCH USE ONLY - NON-COMMERCIAL LICENSE** Soccer QA 4B is a unified video question-answering model specifically designed for soccer video understanding. ## Model Description This model can answer questions about soccer videos by analyzing visual content and generating natural language responses. **Example:** - **Input**: Video + "What unfolded during the game in the video?" - **Output**: "During the game, there was a foul committed by a player from the yellow-jerseyed team, leading to a yellow card being issued..." ## Architecture - **Vision Encoder**: DWT-VJEPA2-based video encoder (vit_giant, 1408 dim) - **Text Model**: LLaMA 3.2-3B with LoRA fine-tuning - **Vision-Text Bridge**: Learned projection layer (1408 → 2048 → 3072) - **Specialization**: Fine-tuned on soccer video QA data ## Usage (Helper functions are in repo) ```python from soccer_qa_inference import SoccerQA model = SoccerQA("/path/to/model") answer = model.ask("video.mp4", "Was this a Foul?", max_tokens=45) print(answer) ``` ## Model Details - **Parameters**: ~4B total - **Input**: Video files (16 frames, 256x256) + text questions - **Output**: Natural language answers - **Domain**: Soccer/football video analysis - **Context**: Handles complex game situations, player actions, fouls, etc. ## Training Data - Soccer video clips with question-answer pairs - Covers various game situations: fouls, shots, saves, player actions - Annotated with detailed descriptions of game events ## Limitations - Research use only, no commercial applications - Optimized specifically for soccer content - May not generalize well to other sports or video domains - Requires high-quality video input for best results ## License CC-BY-NC-4.0 - Research use only, no commercial applications permitted. ## Citation ```bibtex @misc{soccer-qa-4b-2025, title={Soccer QA 4B: Video Question Answering for Soccer Analysis}, author={Varun Kodathala, Sports Vision}, year={2025}, note={Research model for soccer video understanding} } ```

Source context: 17 downloads · 3 likes · Pipeline video-text-to-text · Library transformers · Repo sportsvision/soccer-qa-4b

Source context: 12 downloads · 3 likes · Pipeline video-text-to-text · Library transformers · Repo sportsvision/soccer-qa-4b