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An unoffical implementation of "MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities"

License: Apache License 2.0

Python 2.46% Jupyter Notebook 97.53% Shell 0.01%
anomaly-detection deeplearning defect-detection memory-based segmentation pytorch semi-supervised-learning

memseg-defect-detection's Introduction

MemSeg-Defect-Detection

An unofficial implementation of MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities using PyTorch.

TO USE

  1. Clone the repository
git clone https://github.com/ntkhoa95/MemSeg-Defect-Detection
cd MemSeg-Defect-Detection
  1. Download two datasets
  • Describable Textures Dataset (DTD)
https://www.robots.ox.ac.uk/~vgg/data/dtd/
  • MVTec Dataset
https://www.mvtec.com/company/research/datasets/mvtec-ad
  1. Run Before running the training program, please configure the datasets directory in the "./configs" folder
python main.py --object_name capsule
  1. Inference Mode
voila "inference.ipynb" --port 8866 --Voila.ip 127.0.0.1

RESULTS

Using batch training of 8 MVTec Dataset

target AUROC-image AUROC-pixel AUPRO-pixel
0 leather 100 99.23 98.54
1 wood
2 carpet
3 capsule 97.89 98.48 95.69
4 cable
5 metal_nut
6 tile
7 grid
8 bottle 100 98.59 95.10
9 zipper
10 transistor
11 hazelnut
12 pill
Average

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}
}

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