DRepT — Official PyTorch Implementation

This is the official implementation of the paper "DRepT: Anomaly Detection Based on Transfer of Defect Representation with Transmittance Mask" in 2023 International Joint Conference on Neural Networks (IJCNN) [1] Paper.

1. Overview

(1) Abstract

The purpose is to improve the accuracy of anomaly detection for the target domain in surface anomaly detection (i.e., defect detection).
DRepT uses an anomaly image in the source domain and transfers its defect representation to a normal image in the target domain.
As a result, an anomaly image for the target domain is generated with high quality, and these anomaly images can be used for training the anomaly detection model.

(2) Data available for training

Source domain: Normal images and Anomaly images (without Ground Truth)
Target domain: Normal images only

2. Preparation

(1) Requirement

$ pip3 install torch torchvision numpy pillow tqdm tb-nightly future natsort opencv-python

(2) Git clone

$ git clone https://github.com/koba-jon/DRepT.git
$ cd DRepT

(3) Set dataset

Example of MVTecAD[2]:

$ ln -s <mvtec_anomaly_detection_dataset_path> ./datasets/mvtec_anomaly_detection
$ bash datasets/MVTecAD_convert.sh

3. Usage

Example of MVTecAD[2] (Source: Carpet / Tile / Wood , Target: Leather):

(A) Train and save the stage 1 networks

$ bash scripts/MVTecAD/A_stage1/carpet.sh
$ bash scripts/MVTecAD/A_stage1/tile.sh
$ bash scripts/MVTecAD/A_stage1/wood.sh

(B) Train and save the GMMs with the stage 1 networks

$ bash scripts/MVTecAD/B_stage2/carpet.sh
$ bash scripts/MVTecAD/B_stage2/tile.sh
$ bash scripts/MVTecAD/B_stage2/wood.sh

(C) Copy the GMMs as the models of source domain

$ bash scripts/MVTecAD/C_set/leather.sh

(D) Train and save the stage 3 network

$ bash scripts/MVTecAD/D_stage3/leather.sh

(E) Calculate AUROC with the stage 3 networks

$ bash scripts/MVTecAD/E_test/leather.sh

(X) Transfer the defect representation (GMM) from source to target domain

Example of "Carpet to Leather":

$ bash scripts/MVTecAD/X_transfer/22.sh

4. Citation

If you use this code for your research, please cite the following paper:

@inproceedings{KobayashiIJCNN2023,
  author={Kobayashi, Hiroki and Hashimoto, Manabu},
  title={DRepT: Anomaly Detection Based on Transfer of Defect Representation with Transmittance Mask},
  booktitle={2023 International Joint Conference on Neural Networks (IJCNN)},
  year={2023},
}

5. Reference

[1] H. Kobayashi and M. Hashimoto, "DRepT: Anomaly Detection Based on Transfer of Defect Representation with Transmittance Mask", 2023 International Joint Conference on Neural Networks (IJCNN), 2023.
[2] P. Bergmann, K. Batzner, M. Fauser, D. Sattlegger, and C. Steger, "The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection", International Journal of Computer Vision, Vol.129, issue 4, pp.1038-1059, 2021.