VQAv2 (test)
VQA v2.0 (Visual Question Answering v2.0) is a balanced dataset designed to counter language priors in visual question answering. It consists of complementary image pairs where the same question yields different answers, forcing models to rely on visual understanding rather than language bias. The dataset contains 1,105,904 questions across 204,721 COCO images, requiring understanding of vision, language, and commonsense knowledge.
Llama 3.2 11B Instruct from Meta currently leads the VQAv2 (test) leaderboard with a score of 0.752 across 1 evaluated AI models.
Llama 3.2 11B Instruct leads with 75.2%.
Progress Over Time
Interactive timeline showing model performance evolution on VQAv2 (test)
VQAv2 (test) Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 11B | 128K | $0.05 / $0.05 |
FAQ
Common questions about VQAv2 (test).
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