Official Implementation of AnoVL (Updating)

AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization.

Dataset Preparation

MVTec AD

• Download and extract MVTec AD into data/mvtec
• runpython data/mvtec.py to obtain data/mvtec/meta.json

data
├── mvtec
    ├── meta.json
    ├── bottle
        ├── train
            ├── good
                ├── 000.png
        ├── test
            ├── good
                ├── 000.png
            ├── anomaly1
                ├── 000.png
        ├── ground_truth
            ├── anomaly1
                ├── 000.png

VisA

• Download and extract VisA into data/visa
• runpython data/visa.py to obtain data/visa/meta.json

data
├── visa
    ├── meta.json
    ├── candle
        ├── Data
            ├── Images
                ├── Anomaly
                    ├── 000.JPG
                ├── Normal
                    ├── 0000.JPG
            ├── Masks
                ├── Anomaly
                    ├── 000.png

Test

sh test_zero_shot.sh

Acknowledgements

We thank clip, open_clip, WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation, A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD for providing assistance for our research.

Citation

@article{anovl,
  title={AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization},
  author={Deng, Hanqiu and Zhang, Zhaoxiang and Bao, Jinan and Li, Xingyu},
  journal={arXiv preprint arXiv:2308.15939},
  year={2023}
}