swMATH ID: 41239
Software Authors: Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier
Description: PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization. We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.
Homepage: https://arxiv.org/abs/2011.08785
Source Code:  https://github.com/taikiinoue45/PaDiM
Related Software: CFLOW-AD; GANomaly; TensorFlow; PyTorch Lightning; Scikit; Albumentations; PyOD; Detectron; MMDetection; OpenVino; PyTorch; Python; Anomalib
Cited in: 0 Documents