ntkhoa95 / memseg-defect-detection

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

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

2. 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

3. Run Before running the training program, please configure the datasets directory in the "./configs" folder

python main.py --object_name capsule

4. 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}
}

memseg-defect-detection's Issues

Is there a problem with Figure 4 in the paper