search

TooTouch / MemSeg

Unofficial re-implementation of MemSeg for Anomaly Detection
MIT License
184 stars 31 forks source link

readme

MemSeg

Unofficial re-implementation for MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

Environments

einops==0.5.0
timm==0.5.4
wandb==0.12.17
omegaconf
imgaug==0.4.0

Process

1. Anomaly Simulation Strategy

2. Model Process

Run

Example

python main.py configs=configs.yaml DATASET.target=bottle

Demo

voila "[demo] model inference.ipynb" --port ${port} --Voila.ip ${ip}

Results

target AUROC-image AUROC-pixel AUPRO-pixel
leather 100 98.83 99.09
pill 97.05 98.29 97.96
carpet 99.12 97.54 97.02
hazelnut 100 97.78 99
tile 99.86 99.38 98.81
cable 92.5 82.3 87.31
toothbrush 100 99.28 98.56
transistor 96.5 76.29 86.06
zipper 99.95 97.94 97.26
metal_nut 99.46 88.48 95
grid 99.83 98.37 98.53
bottle 100 98.79 98.36
capsule 95.41 98.43 97.73
screw 94.86 95.08 94
wood 100 97.54 97.62
Average 98.3 94.96 96.15

Citation

@article{DBLP:journals/corr/abs-2205-00908,
  author    = {Minghui Yang and
               Peng Wu and
               Jing Liu and
               Hui Feng},
  title     = {MemSeg: {A} semi-supervised method for image surface defect detection
               using differences and commonalities},
  journal   = {CoRR},
  volume    = {abs/2205.00908},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2205.00908},
  doi       = {10.48550/arXiv.2205.00908},
  eprinttype = {arXiv},
  eprint    = {2205.00908},
  timestamp = {Tue, 03 May 2022 15:52:06 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2205-00908.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}