310万张工业图像数据集，专为分布外检测研究设计
ICONIC-444: A 3.1-Million-Image Dataset for OOD Detection Research
- 构建310万张工业图像数据集，覆盖444类复杂场景
- 涵盖从近域到远域分布外的多级难度，支持精细与粗粒度任务
- 提供22种主流方法基线，助力分布外检测研究
当前分布外（OOD）检测研究受限于缺乏大规模、高质量且类别定义清晰的数据集，尤其在不同难度级别（近-远域OOD）下难以支持细粒度与粗粒度视觉任务。为此，我们提出ICONIC-444（Image Classification and OOD Detection with Numerous Intricate Complexities），一个包含超过310万张RGB图像、覆盖444个类别的大规模工业图像数据集，专为OOD检测研究而设计。图像由原型工业分拣机采集，真实模拟实际应用场景。该数据集通过结构化、多样化的数据，填补现有数据集空白，支持从简单到复杂的各类任务评估。我们在数据集中定义了四个基准任务，用于评测和推动OOD检测研究，并为22种先进后处理式OOD检测方法提供了基线结果。
原文摘要 · Abstract (English)
Current progress in out-of-distribution (OOD) detection is limited by the lack of large, high-quality datasets with clearly defined OOD categories across varying difficulty levels (near- to far-OOD) that support both fine- and coarse-grained computer vision tasks. To address this limitation, we introduce ICONIC-444 (Image Classification and OOD Detection with Numerous Intricate Complexities), a specialized large-scale industrial image dataset containing over 3.1 million RGB images spanning 444 classes tailored for OOD detection research. Captured with a prototype industrial sorting machine, ICONIC-444 closely mimics real-world tasks. It complements existing datasets by offering structured, diverse data suited for rigorous OOD evaluation across a spectrum of task complexities. We define four reference tasks within ICONIC-444 to benchmark and advance OOD detection research and provide baseline results for 22 state-of-the-art post-hoc OOD detection methods.
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