CFA for Target-Oriented Anomaly Localization

PyTorch implementation of CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization (CFA).

Getting Started

Install packages with:

$ pip install -r requirements.txt

Dataset

Prepare industrial image as:

train data:
    dataset_path/class_name/train/good/any_filename.png
    [...]

test data:
    dataset_path/class_name/test/good/any_filename.png
    [...]

    dataset_path/class_name/test/defect_type/any_filename.png
    [...]

How to train

Example

python trainer_cfa.py --class_name all --data_path [/path/to/dataset/] --cnn wrn50_2 --size 224 --gamma_c 1 --gamma_d 1

Performance

WideResNet-50

R : resize. C : crop

	R+C	R	CFA++
bottle	100 / 98.6	100 / 98.9	100 / 98.9
cable	99.8 / 98.7	99.8 / 99.0	99.8 / 99.0
capsule	97.3 / 98.9	99.2 / 99.1	99.2 / 99.1
carpet	99.5 / 98.7	99.4 / 99.0	99.5 / 99.0
grid	99.2 / 97.8	99.9 / 98.1	99.9 / 98.1
hazelnut	100 / 98.6	100 / 98.9	100 / 98.9
leather	100 / 99.1	100 / 99.3	100 / 99.3
metalnut	100 / 98.8	100 / 99.1	100 / 99.1
pill	97.9 / 98.6	97.9 / 98.8	97.9 / 98.8
screw	97.3 / 99.0	93.5 / 98.8	97.3 / 99.0
tile	99.4 / 95.8	100 / 96.3	100 / 96.3
toothbrush	100 / 98.8	97.2 / 99.1	100 / 99.1
transistor	100 / 98.3	100 / 98.4	100 / 98.4
wood	99.7 / 94.8	99.2 / 95.0	99.7 / 95.0
zipper	99.6 / 98.6	99.5 / 99.0	99.6 / 99.0
avg.	99.3 / 98.2	99.0 / 98.5	99.5 / 98.5

Reference

[1] https://github.com/byungjae89/SPADE-pytorch

[2] https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master

[3] https://github.com/pytorch/vision/tree/main/torchvision/models

[4] https://github.com/lukasruff/Deep-SVDD-PyTorch

Citation

@article{lee2022cfa,
  title={CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization},
  author={Lee, Sungwook and Lee, Seunghyun and Song, Byung Cheol},
  journal={arXiv preprint arXiv:2206.04325},
  year={2022}
}