CPR

Official PyTorch implementation of CPR

Datasets

We use the MVTec AD dataset for experiments. And use DTD data set to simulate anomalous image.

The data directory is as follows:

data
├── dtd
│   ├── images
│   ├── imdb
│   └── labels
└── mvtec
    ├── bottle
    │   ├── ground_truth
    │   ├── license.txt
    │   ├── readme.txt
    │   ├── test
    │   └── train
    ...
    └── zipper
        ├── ground_truth
        ├── license.txt
        ├── readme.txt
        ├── test
        └── train

Installation

pytorch

conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

pip install -r requirements.txt

generate foreground and global retrieval result

python tools/generate_foreground.py
python tools/generate_retrieval.py

Training

The Training code will be published after the paper is accepted.

Testing

python test.py -fd log/foreground_mvtec_DenseNet_features.denseblock1_320/ --checkpoints weights/{category}.pth

Pretrained Checkpoints

Download pretrained checkpoints here and put the checkpoints under <project_dir>/weights/.

Baidu Netdisk: https://pan.baidu.com/s/1FTE4b2G8nVZt4lUyaP-kIQ?pwd=ky7j

Acknowledgement

We borrow some codes from PatchCore, MemSeg and SuperPoint

Citation

@misc{li2023target,
      title={Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval}, 
      author={Hanxi Li and Jianfei Hu and Bo Li and Hao Chen and Yongbin Zheng and Chunhua Shen},
      year={2023},
      eprint={2308.06748},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}