Create README.md (#2)
Browse files- Create README.md (95aeabbeab2ab0bebcab0e723614de0a669c92c6)
Co-authored-by: Jiayuan Rao ι₯ΆηζΊ <[email protected]>
README.md
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---
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license: cc-by-sa-4.0
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---
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# 2026 Soccernet Challenge - VQA Overview
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## Task
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Soccernet-VQA is a challenge focused on **multimodal (text, image, video) multiple-choice question answering**, covering 14 distinct soccer understanding tasks. These tasks include assessing background knowledge of players and teams, determining camera status, classifying actions, recognizing fouls, and many other complex scenarios.
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More details could be found at:
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- π 2026 SoccerNet Challenge - VQA: [DevKit](https://github.com/jyrao/SoccerAgent) β
[Rules](https://github.com/jyrao/SoccerAgent/blob/main/Challenge.md) β
[Eval (Test)](https://www.codabench.org/competitions/11086/#/results-tab) β
[Eval (Challenge)](https://www.codabench.org/competitions/11087/)
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- π Paper Relevant Links: [WebPage](https://jyrao.github.io/SoccerAgent) β
[Benchmark](https://huggingface.co/datasets/Homie0609/SoccerBench) β
[Database](https://huggingface.co/datasets/Homie0609/SoccerWiki)
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## Data
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Both the test phase and challenge phase are supported by 500 unique QA pairs, which span all 14 aforementioned tasks. You can download the test set and challenge set on this [huggingface page](https://huggingface.co/datasets/SoccerNet/SN-VQA-2026).
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Each QA pair contains three core components in its dictionary:
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- *`Q`*: The question content.
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- *`materials`*: Paths to relevant images or videos.
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- *`Ox`* (e.g., O1, O2): The multiple-choice options.
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An example of a QA pair is shown below:
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```
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{
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"Q": "How many appearances did the midfielder who is replacing Antoine Griezmann in this video make for Atletico Madrid from 2002 to 2018?",
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"materials": [
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"materials/q12/SoccerReplay-1988/europe_champions-league_2023-2024/2023-11-07_atletico-de-madrid-celtic-fc-champions-league/2_19_01.mp4"
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],
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"O1": "25 appearances",
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"O2": "7 appearances",
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"O3": "18 appearances",
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"O4": "13 appearances"
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}
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```
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## Evaluation
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As for this close-ended QA task, we directly use the accuracy as the evaluation metric:
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$$
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\text{score} = \frac{\text{number of correct answers}}{500}
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$$
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## Baseline
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To facilitate benchmarking, we provide two frequently used models (**Qwen2.5VL** and **GPT-4o**) to infer directly as our baselines. Also, the SoccerAgent pipeline with multi-agent thoughts could be regarded as baseline as well, they can all be found in our [Official Github Repo](https://github.com/jyrao/SoccerAgent).
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## Prize
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The Rank 1 submission of the challenge set can finally win the $1000 prize sponsored by [KNQ Technology](https://knq.ai/).
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