  We present a transformer-based image anomaly detection and localization
network. Our proposed model is a combination of a reconstruction-based approach
and patch embedding. The use of transformer networks helps to preserve the
spatial information of the embedded patches, which are later processed by a
Gaussian mixture density network to localize the anomalous areas. In addition,
we also publish BTAD, a real-world industrial anomaly dataset. Our results are
compared with other state-of-the-art algorithms using publicly available
datasets like MNIST and MVTec.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning