Papers › PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier
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.
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Code
Syntology Ran 7 of 7 code samples harvested from 4 repositories linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 4 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
7 samples harvested; 7 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | Hyper-Kvasir Dataset | PaDiM | AUC | 0.923 | #4 of 6 | Archive leaderboard | report |
| Anomaly Detection | LAG | PaDiM | AUC | 0.688 | #5 of 5 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PaDiM | Detection AUROC | 97.9 | #70 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PaDiM-WR50-Rd550 | Detection AUROC | 95.3 | #90 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PaDiM-WR50-Rd550 | FPS | 4.4 | #90 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PaDiM-WR50-Rd550 | Segmentation AUROC | 97.5 | #90 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PaDiM-R18-Rd100 | Segmentation AUROC | 96.7 | #136 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | PaDiM | Segmentation AUPRO (until 30% FPR) | 85.9 | #43 of 50 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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