Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. However, the real-world effectiveness of MUAD methods is questioned due to limitations in current Industrial Anomaly Detection (IAD) datasets. These datasets contain numerous classes that are unlikely to be produced by the same factory and fail to cover multiple structures or appearances. Additionally, the defects do not reflect real-world characteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial Anomaly Detection (HSS-IAD) dataset, which contains 8,580 images of metallic-like industrial parts and precise anomaly annotations. These parts exhibit variations in structure and appearance, with subtle defects that closely resemble the base materials. We also provide foreground images for synthetic anomaly generation. Finally, we evaluate popular IAD methods on this dataset under multi-class and class-separated settings, demonstrating its potential to bridge the gap between existing datasets and real factory conditions. The dataset is available atthis https URL.
View on arXiv@article{wang2025_2504.12689,
title={ HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset },
author={ Qishan Wang and Shuyong Gao and Junjie Hu and Jiawen Yu and Xuan Tong and You Li and Wenqiang Zhang },
journal={arXiv preprint arXiv:2504.12689},
year={ 2025 }
}