PaDiM

Contents

• Introduction
• Getting Started
  • Requirements
  • Local Install
• MVTec AD
  • Results
    • Image-Level AUC
    • Pixel-Level AUC
    • Image F1 Score
  • Train (e.g bottle)
  • Test (e.g bottle)
• Folder
• Train
• Test
• Contributing
• Credit
  • PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

Introduction

PyTorch unofficial implements PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization paper.

Getting Started

Requirements

• Python 3.10+
• PyTorch 2.0.0+
• CUDA 11.8+
• Ubuntu 22.04+

Local Install

git clone https://github.com/Lornatang/PaDiM.git
cd PaDiM
pip install -r requirements.txt
pip install -e .

MVTec AD

Results

Image-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.891	0.945	0.857	0.982	0.950	0.976	0.994	0.844	0.901	0.750	0.961	0.863	0.759	0.889	0.920	0.780
Wide ResNet-50	0.950	0.995	0.942	1.0	0.974	0.993	0.999	0.878	0.927	0.964	0.989	0.939	0.845	0.942	0.976	0.882

Pixel-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.968	0.984	0.918	0.994	0.934	0.947	0.983	0.965	0.984	0.978	0.970	0.957	0.978	0.988	0.968	0.979
Wide ResNet-50	0.979	0.991	0.970	0.993	0.955	0.957	0.985	0.970	0.988	0.985	0.982	0.966	0.988	0.991	0.976	0.986

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.916	0.930	0.893	0.984	0.934	0.952	0.976	0.858	0.960	0.836	0.974	0.932	0.879	0.923	0.796	0.915
Wide ResNet-50	0.951	0.989	0.930	1.0	0.960	0.983	0.992	0.856	0.982	0.937	0.978	0.946	0.895	0.952	0.914	0.947

Train (e.g bottle)

Prepare the dataset, dataset structure see here

python tools/train.py ./configs/mvtec.yaml

Test (e.g bottle)

python tools/eval.py ./configs/mvtec.yaml

more visualization results see results/eval/mvtec_bottle/visual

Folder

Train

Prepare the dataset, dataset structure see here

python tools/train.py ./configs/folder.yaml

Test

python tools/eval.py ./configs/folder.yaml

more visualization results see results/eval/folder/visual

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Credit

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

Thomas Defard, Aleksandr Setkov, Angelique Loesch, Romaric Audigier

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.

[Paper]

@article{DBLP:journals/corr/abs-2011-08785,
  author       = {Thomas Defard and
                  Aleksandr Setkov and
                  Angelique Loesch and
                  Romaric Audigier},
  title        = {PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection
                  and Localization},
  journal      = {CoRR},
  volume       = {abs/2011.08785},
  year         = {2020},
  url          = {https://arxiv.org/abs/2011.08785},
  eprinttype    = {arXiv},
  eprint       = {2011.08785},
  timestamp    = {Wed, 18 Nov 2020 16:48:35 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2011-08785.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}