IshiharaColorBench is a benchmark designed to measure the pure color perception capability of Large Vision-Language Models (LVLMs). The dataset's specific size, format, and authorship are unknown. It is hosted on Kaggle.
Use Cases
- Benchmarking LVLM color recognition accuracy based on the described color perception capability
- Evaluating model robustness to color-based tasks as implied by the benchmark's purpose
- Training LVLMs on color perception as a specific visual skill
Strengths
- The dataset is explicitly designed as a benchmark for a specific AI capability (color perception).
- It targets a relevant and growing model category (Large Vision-Language Models).
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.