PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

Code

• github.com/openvinotoolkit/anomalib
  pytorch★ 5,507
• github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master
  pytorch★ 473
• github.com/open-edge-platform/geti
  pytorch★ 467
• github.com/rvorias/ind_knn_ad
  pytorch★ 165
• github.com/OpenAOI/anodet
  pytorch★ 83
• github.com/Burf/tfdetection
  tf★ 56
• github.com/taikiinoue45/PaDiM
  pytorch★ 40
• github.com/Pangoraw/PaDiM
  pytorch★ 30
• github.com/Ultranity/Anomaly.Paddle
  paddle★ 28
• github.com/JohnnyHopp/PaDiM-EfficientNetV2
  pytorch★ 25

Abstract

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.

Tasks

Benchmark Results

Reproductions