  Industrial anomaly detection (IAD) has garnered significant attention and
experienced rapid development. However, the recent development of IAD approach
has encountered certain difficulties due to dataset limitations. On the one
hand, most of the state-of-the-art methods have achieved saturation (over 99%
in AUROC) on mainstream datasets such as MVTec, and the differences of methods
cannot be well distinguished, leading to a significant gap between public
datasets and actual application scenarios. On the other hand, the research on
various new practical anomaly detection settings is limited by the scale of the
dataset, posing a risk of overfitting in evaluation results. Therefore, we
propose a large-scale, Real-world, and multi-view Industrial Anomaly Detection
dataset, named Real-IAD, which contains 150K high-resolution images of 30
different objects, an order of magnitude larger than existing datasets. It has
a larger range of defect area and ratio proportions, making it more challenging
than previous datasets. To make the dataset closer to real application
scenarios, we adopted a multi-view shooting method and proposed sample-level
evaluation metrics. In addition, beyond the general unsupervised anomaly
detection setting, we propose a new setting for Fully Unsupervised Industrial
Anomaly Detection (FUIAD) based on the observation that the yield rate in
industrial production is usually greater than 60%, which has more practical
application value. Finally, we report the results of popular IAD methods on the
Real-IAD dataset, providing a highly challenging benchmark to promote the
development of the IAD field.
