Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement of IAD algorithms is severely hindered by the limitations of existing public benchmarks. Current datasets often suffer from restricted category diversity and insufficient scale, leading to performance saturation and poor model transferability in complex, real-world scenarios. To bridge this gap, we introduce Real-IAD Variety, the largest and most diverse IAD benchmark. It comprises 198,950 high-resolution images across 160 distinct object categories. The dataset ensures unprecedented diversity by covering 28 industries, 24 material types, 22 color variations, and 27 defect types. Our extensive experimental analysis highlights the substantial challenges posed by this benchmark: state-of-the-art multi-class unsupervised anomaly detection methods suffer significant performance degradation (ranging from 10% to 20%) when scaled from 30 to 160 categories. Conversely, we demonstrate that zero-shot and few-shot IAD models exhibit remarkable robustness to category scale-up, maintaining consistent performance and significantly enhancing generalization across diverse industrial contexts. This unprecedented scale positions Real-IAD Variety as an essential resource for training and evaluating next-generation foundation IAD models.

翻译：工业异常检测（IAD）是保障运行安全、维持产品质量和优化制造效率的基石。然而，现有公共基准的局限性严重阻碍了IAD算法的发展。当前数据集普遍存在类别多样性受限与规模不足的问题，导致在复杂现实场景中出现性能饱和及模型可迁移性差的现象。为弥补这一差距，我们提出了Real-IAD Variety——规模最大、多样性最丰富的IAD基准数据集。该数据集包含160个不同物体类别的198,950张高分辨率图像，通过覆盖28个行业、24种材料类型、22种颜色变体及27种缺陷类型，确保了前所未有的多样性。我们广泛的实验分析凸显了该基准带来的重大挑战：当类别规模从30扩展到160时，最先进的多类别无监督异常检测方法性能显著下降（降幅达10%至20%）。相反，我们证明了零样本与少样本IAD模型对类别扩展表现出卓越的鲁棒性，能保持稳定性能并显著提升跨多样化工业场景的泛化能力。这一空前规模使Real-IAD Variety成为训练和评估下一代基础IAD模型的重要资源。