This is the official implementation for the paper 'Frequency-aware Image Restoration for Industrial Visual Anomaly Detection'. https://arxiv.org/abs/2309.07068 If you have any questions, you could contact [email protected]

The method is evaluated on:
the MVTec AD dataset: https://www.mvtec.com/company/research/datasets/mvtec-ad
the VisA dataset: https://github.com/amazon-science/spot-diff

The original code uses the DTD dataset to create synthesized anomalies, so you first need to download it
: the DTD dataset (optional): https://www.robots.ox.ac.uk/~vgg/data/dtd/
Then

python train.py --gpu_id 0 --obj_id -1 --lr 0.0001 --bs 8 --epochs 800 --data_path /home/b211-3090ti/Anomaly-Dataset/mvtec_ad/ --anomaly_source_path /home/b211-3090ti/Anomaly-Dataset/dtd/images --log_path /home/b211-3090ti/FAIR/checkpoints_mvtecad/ --checkpoint_path /home/b211-3090ti/FAIR/checkpoints_mvtecad/ --visualize

Change all the involved paths to your own paths

activate line 16 and line 76 in data_loaderbhpfnoDTD.py

self.images = sorted(glob.glob(root_dir+"/*/*.JPG")) mask_file_name = file_name.split(".")[0]+".png"

It's also feasible to train it without extra data, just activate line 3 in train.py:

from data_loaderbhpfnoDTD import MVTecTrainDataset

Then

python train.py --gpu_id 0 --obj_id -1 --lr 0.0001 --bs 8 --epochs 800 --data_path /home/b211-3090ti/Anomaly-Dataset/mvtec_ad/ --log_path /home/b211-3090ti/FAIR/checkpoints_mvtecad/ --checkpoint_path /home/b211-3090ti/FAIR/checkpoints_mvtecad/ --visualize

python test.py --gpu_id 0 --base_model_name FAIR_0.0001_800_bs8 --data_path /home/b211-3090ti/Anomaly-Dataset/mvtec_ad/ --checkpoint_path /home/b211-3090ti/FAIR/checkpoints_mvtecad/

Change all the involved paths to your own paths

If you want to visualize the results, add

--saveimages

MVTec AD

https://drive.google.com/file/d/1uwodfQZSXNq_TToio0r8gflUCg1EwSJv/view?usp=sharing

https://pan.baidu.com/s/1y8eocVy8FKf88nCkyd_4_A jc5j

VisA

https://drive.google.com/file/d/1DKYOqZAE-wbjRamk8xBhKB1Q2qswYxFR/view?usp=sharing

https://pan.baidu.com/s/1uS6etphpGiVDKTGPYjDdCg qecd

We use the codes from https://github.com/VitjanZ/DRAEM, https://github.com/taikiinoue45/RIAD, and https://www.mvtec.com/company/research/datasets/mvtec-3d-ad

A big thanks to their great work