Awesome

DiffAD

[ICCV2023] Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model

@inproceedings{zhang2023unsupervised,
  title={Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model},
  author={Zhang, Xinyi and Li, Naiqi and Li, Jiawei and Dai, Tao and Jiang, Yong and Xia, Shu-Tao},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6782--6791},
  year={2023}
}

Method overview

Installation

conda env create -f environment.yaml
conda activate DiffAD

Dataset

Following DRAEM, we use the MVTec-AD and DTD dataset. You can run the download_dataset.sh script from the project directory to download the MVTec and the DTD datasets to the datasets folder in the project directory:

./scripts/download_dataset.sh

Training

Reconstruction sub-network

The reconstrucion sub-network is based on the latent diffusion model.

Training Auto-encoder

cd rec_network
CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/kl.yaml -t --gpus 0,  

Training LDMs

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/mvtec.yaml -t --gpus 0 -max_epochs 4000, 

Discriminative sub-network

cd seg_network
CUDA_VISIBLE_DEVICES=<GPU_ID> python train.py --gpu_id 0 --lr 0.001 --bs 32 --epochs 700 --data_path ./datasets/mvtec/ --anomaly_source_path ./datasets/dtd/images/ --checkpoint_path ./checkpoints/obj_name --log_path ./logs/

Evaluating

Reconstrucion performance

After training the reconstruction sub-network, you can test the reconstruction performance with the anomalous inputs:

python scripts/mvtec.py

For some samples with severe deformations, such as missing transistors, you can add some noise to the anomalous conditions to adjust the sampling.

Anomaly segmentation

cd seg_network
python test.py --gpu_id 0 --base_model_name "seg_network" --data_path ./datasets/mvtec/ --checkpoint_path ./checkpoints/obj_name/