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DSR - A dual subspace re-projection network for surface anomaly detection

This repository contains the implementation of DSR as proposed in the paper DSR -- A dual subspace re-projection network for surface anomaly detection

Requirements

You can create a conda environment with: conda create --name <env> --file requirements.txt

But the main packages used are:

PyTorch 1.11, opencv-python, sklearn, numpy

DSR evaluation on MVTec

Downloading the MVTec benchmark

Set up the MVTec AD benchmark by downloading it from: https://www.mvtec.com/company/research/datasets/mvtec-ad, and extracting it. For training and evaluation pass the base directory of the extracted files (for example ./data/mvtec/) as the --data_path argument.

Download the pretrained models

Download the pretrained models and extract the zip so that the checkpoints folder will be located in the base directory of this repository.

Download link: https://drive.google.com/file/d/15plhikrUjYCcx23JVxxBKb-HBwKAb8UK/view?usp=sharing

Running the evaluation

#BASE_PATH -- the base directory of mvtec
#i -- the gpu id used for evaluation
python test_dsr.py $i $BASE_PATH DSR

Training DSR

#BASE_PATH -- the base directory of mvtec
#OUT_PATH -- where the trained models will be saved
#i -- the index of the object class in the obj_batch list in train_dsr.py
python train_dsr.py --gpu_id 0 --obj_id $i --lr 0.0002 --bs 8 --epochs 100 --data_path $BASE_PATH --out_path $OUT_PATH