Anomaly Detection via Self-Organizing Map

Introduction

This paper is accepted by ICIP 2021.

SOMAD is a novel unsupervised anomaly detection approach based on Self-organizing Map (SOM)

For more details, please refer to our paper.

Requirements

• torch
• torchvision
• numpy
• opencv

How to use

python somad.py --dataset mvtec

Dataset

we use the MVTec dataset, please prepare dataset like below

./mvtec/bottle
./mvtec/xxx
...

TODO List

• Release the models trained using MVTec dataset
• Update train doc

Citation

If you find SOMAD useful in your research, please consider citing:

@article{Li2021AnomalyDV,
  title={Anomaly Detection Via Self-Organizing Map},
  author={Ning Li and Kaitao Jiang and Zhiheng Ma and Xing Wei and Xiaopeng Hong and Yihong Gong},
  journal={2021 IEEE International Conference on Image Processing (ICIP)},
  year={2021},
  pages={974-978}
}