DSVDD-CPM

Anomaly Detection Control through One-class Learning in Defect
Inspection Systems (paper under review)

https://doi.org/10.5302/J.ICROS.2025.24.0280

Journal of Institute of Control, Robotics and Systems 2025, 31(4), 328-333
ISSN:1976-5622
eISSN:2233-4335

Usage

Datasets

Our research currently has completed AD experiments on the MNIST (yann.lecun), CIFAR-10 (cs.toronto) and MVTec-AD (mvtec, kaggle) datasets.

MVTec-AD example by shell script

Run shell script (log file output: ~/DSVDD-CPM/log)

sh mecro.sh mvtecad

or default setting (MVTec-AD)

sh mecro.sh

or with options

sh mecro.sh mvtecad 0.9 True True 0

MVTec-AD example by python execution

Run python execution (ternimal window output)

python ./src/main.py mvtecad mvtecad_LeNet_ELU ./log/mvtecad_test ./data

or with options

python ./src/main.py mvtecad mvtecad_LeNet_ELU ./log/mvtecad_test ./data --seed 1758683904 --device cuda --optimizer_name adam --lr 0.01 --n_epochs 60 --lr_milestone 20 --lr_milestone 50 --batch_size 32 --weight_decay 0.5e-6 --decay_coef 0.9 --linear_decay True --pretrain True --ae_lr 0.01 --ae_n_epochs 75 --ae_lr_milestone 60 --ae_batch_size 32 --ae_weight_decay 0.5e-3 --normal_class 0 --n_jobs_dataloader 0

Run python execution (log file output: ~/DSVDD-CPM/log)

nohup python ./src/main.py mvtecad mvtecad_LeNet_ELU ./log/mvtecad_test ./data --normal_class 0 --decay_coef 0.9 --linear_decay True ./log/mvtecad_test/0/decay_coef=0.9-linear_decay=True.out 2>&1 &

MVTec-AD experiment

CIFAR-10 example by shell script

Run shell script (log file output: ~/DSVDD-CPM/log)

sh mecro.sh cifar10

or with options

sh mecro.sh cifar10 0.9 True True 0

CIFAR-10 example by python execution

Run python execution (ternimal window output)

python ./src/main.py cifar10 cifar10_LeNet_ELU ./log/cifar10_test ./data

or with options

python ./src/main.py cifar10 cifar10_LeNet_ELU ./log/cifar10_test ./data --seed 1170014347 --device cuda --optimizer_name adam --lr 0.0001 --n_epochs 150 --lr_milestone 120 --batch_size 256 --weight_decay 0.5e-6 --decay_coef 0.9 --linear_decay True --pretrain True --ae_lr 0.0001 --ae_n_epochs 350 --ae_lr_milestone 280 --ae_batch_size 256 --ae_weight_decay 0.5e-6 --normal_class 0 --n_jobs_dataloader 0

Run python execution (log file output: ~/DSVDD-CPM/log)

nohup python ./src/main.py cifar10 cifar10_LeNet_ELU ./log/cifar10_test ./data --normal_class 0 --decay_coef 0.9 --linear_decay True ./log/cifar10_test/0/decay_coef=0.9-linear_decay=True.out 2>&1 &

CIFAR-10 experiment

MNIST example by shell script

Run shell script (log file output: ~/DSVDD-CPM/log)

sh mecro.sh mnist

or with options

sh mecro.sh mnist 0.9 True True 0

MNIST example by python execution

Run python execution (ternimal window output)

python ./src/main.py mnist mnist_LeNet ./log/mnist_test ./data --lr 0.0001 --n_epochs 150 --lr_milestone 120 --batch_size 256 --weight_decay 0.5e-6 --decay_coef 0.9 --linear_decay True --pretrain True --ae_lr 0.0001 --ae_n_epochs 150 --ae_lr_milestone 120 --ae_batch_size 256 --ae_weight_decay 0.5e-3 --normal_class 0

Run python execution (log file output: ~/DSVDD-CPM/log)

nohup python ./src/main.py mnist mnist_LeNet ./log/mnist_test ./data --normal_class 0 --decay_coef 0.9 --linear_decay True ./log/mnist_test/0/decay_coef=0.9-linear_decay=True.out 2>&1 &

Citation

If you use our code, please cite the paper below:

@article{전장군2025결함,
  title={결함 검사 시스템에서 단일 클래스 학습을 통한 이상 탐지 제어},
  author={전장군 and 김남기},
  journal={제어로봇시스템학회 논문지},
  volume={31},
  number={4},
  pages={328-333},
  year={2025}
}