OneIG-Bench (English track, often referred to as OneIG-EN) is a benchmark dataset and evaluation suite for text-to-image (T2I) generation models that provides fine-grained, omni-dimensional human/evaluator judgments. It evaluates T2I outputs along five dimensions — Alignment (prompt-image semantic alignment), Text (text rendering and fidelity), Reasoning (knowledge- or logic-based correctness), Style (stylistic fidelity and diversity of styles), and Diversity — and reports both per-dimension scores and an Overall score. The Hugging Face dataset release contains two subsets: an English subset (OneIG-Bench, ~1.12k prompts/instances) and a Chinese subset (OneIG-Bench-ZH, ~1.32k prompts/instances), together covering ~2.44k test cases used by the paper. Metadata on the Hugging Face page lists the task category as text-to-image, the license as CC-BY-NC-4.0, and links to the paper, project page, and code repository.
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