Jayant-Sravan/CountQA
Dataset Summary CountQA is the new benchmark designed to stress-test the Achilles' heel of even the most advanced Multimodal Large Language Models (MLLMs): object counting. While modern AI demonstrates stunning visual fluency, it often fails at this fundamental cognitive skill, a critical blind spot limiting its real-world reliability. This dataset directly confronts that weakness with over 1,500 challenging question-answer pairs built on real-world images, hand-captured to… See the full description on the dataset page: https://huggingface.co/datasets/Jayant-Sravan/CountQA.
| Author / organization | Jayant-Sravan |
|---|---|
| License (as declared) | cc-by-4.0 |
| Downloads | 3K (as reported by Hugging Face) |
| Likes | 5 |
| Created | 8 August 2025 |
| Last updated at source | 15 September 2025 |
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Recent changes
- 16 Oct 2026 Description: Dataset Summary CountQA is the new benchmark designed to stress-test the Achilles' heel of even the most advanced Multimodal Large Language Models (MLLMs): object counting. While modern AI demonstrates stunning visual fluency, it often fail → Dataset Summary CountQA is the new benchmark designed to stress-test the Achilles' heel of even the most advanced Multimodal Large Language Models (MLLMs): object counting. While modern AI demonstrates stunning visual fluency, it often fail
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