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CrowdHuman is a benchmark dataset for evaluating detector performance in crowded scenes. It is large‑scale, richly annotated, and highly diverse, comprising training, validation, and test sets with a total of 470,000 annotated human instances (average 23 persons per image) covering various occlusion conditions. Each person instance is annotated with a head bounding box, a visible region box, and a full‑body box.
