WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

Unofficial implementation of:

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation, CVPR 2023 [Paper]

Citation

If you find the code useful, please consider citing our paper using the following BibTeX entry.

@InProceedings{Jeong_2023_CVPR,
    author    = {Jeong, Jongheon and Zou, Yang and Kim, Taewan and Zhang, Dongqing and Ravichandran, Avinash and Dabeer, Onkar},
    title     = {WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {19606-19616}
}

Related Research

@misc{cao2023segment,
      title={Segment Any Anomaly without Training via Hybrid Prompt Regularization}, 
      author={Yunkang Cao and Xiaohao Xu and Chen Sun and Yuqi Cheng and Zongwei Du and Liang Gao and Weiming Shen},
      year={2023},
      eprint={2305.10724},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Related Repo

Segment Any Anomaly

Prerequisite

Python 3.7, PyTorch 1.10, and more in install.sh

Install python dependencies

sh install.sh

Download MVTec-AD dataset

Follow instructions in https://www.mvtec.com/company/research/datasets/mvtec-ad/

Download Visa dataset

Follow instructions in https://paperswithcode.com/dataset/visa

Run run_winclip.py to reproduce the implementation results

python run_winclip.py

Results

MVTec-AD

MVTec-AD	Reported				Re-implementation
	i-auroc	p-auroc	i-max-f1	p-max-f1	i-auroc	p-auroc	i-max-f1	p-max-f1
carpet	100.00	95.40	99.40	49.70	77.41	88.96	88.44	29.31
grid	98.80	82.20	98.20	18.60	48.87	75.08	85.71	8.40
leather	100.00	96.70	100.00	39.70	97.35	97.35	95.70	29.60
tile	100.00	77.60	99.40	32.60	79.87	75.87	85.25	29.30
wood	99.40	93.40	98.30	51.50	94.74	93.03	92.68	44.65
bottle	99.20	89.50	97.60	58.10	98.65	89.58	96.77	49.36
cable	86.50	77.00	84.50	19.70	53.30	56.23	76.03	10.22
capsule	72.90	86.90	91.40	21.70	62.03	88.56	90.46	9.95
hazelnut	93.90	94.30	89.70	37.60	71.29	94.34	80.00	33.63
metal_nut	97.10	61.00	96.30	32.40	37.59	42.67	89.42	21.67
pill	79.10	80.00	91.60	17.60	73.10	74.67	91.56	11.98
screw	83.30	89.60	87.40	13.50	64.87	90.09	85.61	9.09
toothbrush	87.50	86.90	87.90	17.10	41.94	84.02	84.51	9.26
transistor	88.00	74.70	79.50	30.50	62.25	67.46	60.87	15.95
zipper	91.50	91.60	92.90	34.40	89.31	92.08	90.42	31.48
Average	91.81	85.12	92.94	31.65	70.17	80.67	86.23	22.92

VisA

VisA	Reported				Re-implementation
	i-auroc	p-auroc	i-max-f1	p-max-f1	i-auroc	p-auroc	i-max-f1	p-max-f1
candle	95.40	88.90	89.40	22.50	79.03	86.24	72.36	6.32
capsules	85.00	81.60	83.90	9.20	53.58	62.00	77.22	1.36
cashew	92.10	84.70	88.40	13.20	70.66	79.54	80.99	6.94
chewinggum	96.50	93.30	94.80	41.10	84.94	97.01	83.76	36.17
fryum	80.30	88.50	82.70	22.10	52.60	86.73	80.33	15.17
macaroni1	76.20	70.90	74.20	7.00	49.98	34.37	66.67	0.07
macaroni2	63.70	59.30	69.80	1.00	49.56	31.49	66.67	0.06
pcb1	73.60	61.20	71.00	2.40	55.99	44.04	68.97	0.97
pcb2	51.20	71.60	67.10	4.70	61.58	64.47	69.26	0.70
pcb3	73.40	85.30	71.00	10.30	51.42	68.71	66.45	1.06
pcb4	79.60	94.40	74.90	32.00	78.94	91.86	74.56	22.75
pipe_fryum	69.70	75.40	80.70	12.30	82.80	93.65	83.48	22.45
Average	78.06	79.59	78.99	14.82	64.26	70.01	74.23	9.50

Acknowledgements

This project borrows some code from OpenCLip and CDO, thanks for their admiring contributions~!

caoyunkang/WinClip