Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era

• URL: http://arxiv.org/abs/2511.00540v1
• Date: Sat, 01 Nov 2025 12:58:02 GMT
• Title: Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
• Authors: Wenbing Zhu, Chengjie Wang, Bin-Bin Gao, Jiangning Zhang, Guannan Jiang, Jie Hu, Zhenye Gan, Lidong Wang, Ziqing Zhou, Linjie Cheng, Yurui Pan, Bo Peng, Mingmin Chi, Lizhuang Ma,
• Abstract summary: Real-IAD Variety is the largest and most diverse IAD benchmark, comprising 198,960 high-resolution images across 160 distinct object categories.<n>Its diversity is ensured through comprehensive coverage of 28 industries, 24 material types, and 22 color variations.<n>Real-IAD Variety will be made publicly available to facilitate innovation in this critical field.
• Score: 110.83702639978469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial Anomaly Detection (IAD) is critical for enhancing operational safety, ensuring product quality, and optimizing manufacturing efficiency across global industries. However, the IAD algorithms are severely constrained by the limitations of existing public benchmarks. Current datasets exhibit restricted category diversity and insufficient scale, frequently resulting in metric saturation and limited model transferability to real-world scenarios. To address this gap, we introduce Real-IAD Variety, the largest and most diverse IAD benchmark, comprising 198,960 high-resolution images across 160 distinct object categories. Its diversity is ensured through comprehensive coverage of 28 industries, 24 material types, and 22 color variations. Our comprehensive experimental analysis validates the benchmark's substantial challenge: state-of-the-art multi-class unsupervised anomaly detection methods experience significant performance degradation when scaled from 30 to 160 categories. Crucially, we demonstrate that vision-language models exhibit remarkable robustness to category scale-up, with minimal performance variation across different category counts, significantly enhancing generalization capabilities in diverse industrial contexts. The unprecedented scale and complexity of Real-IAD Variety position it as an essential resource for training and evaluating next-generation foundation models for anomaly detection. By providing this comprehensive benchmark with rigorous evaluation protocols across multi-class unsupervised, multi-view, and zero-/few-shot settings, we aim to accelerate research beyond domain-specific constraints, enabling the development of scalable, general-purpose anomaly detection systems. Real-IAD Variety will be made publicly available to facilitate innovation in this critical field.

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