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UCAD in AAAI-2024

Offical code for Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt in AAAI-2024 [paper link]

Jiaqi Liu*, Kai Wu*, Qiang Nie, Ying Chen, Bin-Bin Gao,Yong Liu, Jinbao Wang, Chengjie Wang, Feng Zheng†

Introduction

UCAD is a novel Unsupervised Continual AD framework and we augment it with SAM.

environment

basic

python>=3.8, torch>=1.12, CUDA>=11.3, timm==0.6.7

install SAM:

pip install git+https://github.com/facebookresearch/segment-anything.git

or clone the repository locally and install with

git clone git@github.com:facebookresearch/segment-anything.git
cd segment-anything; pip install -e .

prepare for training

rename the dataset dir to 'mvtec2d' and create sam senmantic dir (processed mvtec2d-sam-b.zip is provided in repository)

cp -r $mvtec_origin_data_path('./mvtec2d') $mvtec_data_path('./mvtec2d-sam-b')
cd UCAD/segment_anything
python3 dataset_sam.py --sam_type 'vit_b' --sam_checkpoint $your_sam_path --data_path $mvtec_data_path

training and evaluation

environment prepare:

datapath=/hhd3/m3lab/data/mvtec2d 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))

training:

CUDA_VISIBLE_DEVICES=0 python3 run_ucad.py --gpu 0 --seed 0 --memory_size 196 --log_group IM224_UCAD_L5_P01_D1024_M196 --save_segmentation_images --log_project MVTecAD_Results results ucad -b wideresnet50 -le layer2 -le layer3 --faiss_on_gpu --pretrain_embed_dimension 1024 --target_embed_dimension 1024 --anomaly_scorer_num_nn 1 --patchsize 1 sampler -p 0.1 approx_greedy_coreset dataset --resize 224 --imagesize 224 "${dataset_flags[@]}" mvtec $datapath

Parameter

Main contents are contained in three files: ./patchcore/patchcore.py, ./patchcore/vision_transformer.py, and ./run_ucad.py. Whether to save the image, the image size, and the memory size can all be modified in the above training command. Parameters about prompt are in ./patchcore/patchcore.py line 99.

The inference involving a query process, it's slow, and I've commented it out in the code (./run_ucad.py lines 408-509). Training will directly provide the final results, and the inference process merely repeats this step. The final output will consist of two parts, with the lower metrics representing the final results, and the difference between them and the higher metrics results is denoted as FM.

Acknowledgments

This work is supported by the National Key R&D Program of China (Grant NO. 2022YFF1202903) and the National Natural Science Foundation of China (Grant NO. 62122035).

Our benchmark is built on PatchCore and DualPrompt_Pytorch, thanks their extraordinary works!