AnomalyDetection-SoftPatch (NeurIPS2022)

This repository contains codes for the official implementation in PyTorch of NeurIPS 2022 paper "SoftPatch: Unsupervised Anomaly Detection with Noisy Data."

Quick Start

Requirement

Our results were computed using Python 3.8 with packages and respective version noted in requirements.txt.

MVTec-AD

• Default Training. To train SoftPatch on MVTec AD with 0.1 additional noise samples, run

datapath=/path_to_mvtec_folder/mvtec 
datasets=('bottle' 'cable' 'capsule' 'carpet' 'grid' 'hazelnut'
'leather' 'metal_nut' 'pill' 'screw' 'tile' 'toothbrush' 'transistor' 'wood' 'zipper')
dataset_flags=($(for dataset in "${datasets[@]}"; do echo '-d '$dataset; done))

python main.py --dataset mvtec --data_path ../../MVTec --noise 0.1  "${dataset_flags[@]}" --gpu 0

The default setting in run_mvtec.sh runs with 224x224 image size using a WideResNet50-backbone pretrained on ImageNet.

• Expected Performance. Training on 1 GPU (NVIDIA Tesla V100 32GB) results in following performance.

Row Names	image_auroc	pixel_auroc
mvtec_bottle	1.0000	0.9878
mvtec_cable	0.9904	0.9862
mvtec_capsule	0.9654	0.9883
mvtec_carpet	0.9965	0.9920
mvtec_grid	1.0000	0.9939
mvtec_hazelnut	1.0000	0.9906
mvtec_leather	1.0000	0.9931
mvtec_metal_nut	0.9987	0.9845
mvtec_pill	0.9562	0.9798
mvtec_screw	0.9526	0.9944
mvtec_tile	0.9866	0.9645
mvtec_toothbrush	0.9931	0.9860
mvtec_transistor	0.9974	0.9064
mvtec_wood	0.9854	0.9714
mvtec_zipper	0.9753	0.9892
Mean	0.9865	0.9805

• Parameter Setting.

To choose other noise discriminator, use the --weight_method argument with 'lof', 'nearest', 'gaussian' or 'lof_gpu'. 'lof_gpu' supports computing LOF using the GPU which usually faster.

To

BTAD

To train SoftPatch on BTAD, run:

datapath=/path_to_btad_folder/BTAD
datasets=('01' '02' '03')
dataset_flags=($(for dataset in "${datasets[@]}"; do echo '-d '$dataset; done))

python main.py --dataset btad --data_path ../../BTAD --noise 0  "${dataset_flags[@]}" --seed 0 \
--gpu 1 --resize 512 --imagesize 512 --sampling_ratio 0.01

The default setting in run_btad.sh runs with 512x512 image size using a WideResNet50-backbone pretrained on ImageNet.

Row Names	image_auroc	pixel_auroc
btad_01	0.9981	0.9761
btad_02	0.9343	0.9662
btad_03	0.9969	0.9935
Mean	0.9764	0.9786

Comments

• Our codebase for the coreset construction builds heavily on PatchCore codebase. Thanks for open-sourcing!

Citation

Please cite the following paper if this dataset helps your project:

@misc{xisoftpatch,
  title={SoftPatch: Unsupervised Anomaly Detection with Noisy Data},
  author={Xi, Jiang and Liu, Jianlin and Wang, Jinbao and Nie, Qiang and Kai, WU and Liu, Yong and Wang, Chengjie and Zheng, Feng},
  booktitle={Advances in Neural Information Processing Systems}
}

License

This project is licensed under the Apache-2.0 License.