# MemSeg

**Repository Path**: zhen_ming_hui/MemSeg

## Basic Information

- **Project Name**: MemSeg
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-25
- **Last Updated**: 2024-02-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MemSeg
Unofficial re-implementation for [MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities](https://arxiv.org/abs/2205.00908)

# Environments

- Docker image: nvcr.io/nvidia/pytorch:20.12-py3

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


# Process

## 1. Anomaly Simulation Strategy 

- [notebook](https://github.com/TooTouch/MemSeg/blob/main/%5Bexample%5D%20anomaly_simulation_strategy.ipynb)
- Describable Textures Dataset(DTD) [ [download](https://www.google.com/search?q=dtd+texture+dataset&rlz=1C5CHFA_enKR999KR999&oq=dtd+texture+dataset&aqs=chrome..69i57j69i60.2253j0j7&sourceid=chrome&ie=UTF-8) ]

<p align='center'>
    <img width='700' src='https://user-images.githubusercontent.com/37654013/198960273-ba763f40-6b30-42e3-ab2c-a8e632df63e9.png'>
</p>

## 2. Model Process 

- [notebook](https://github.com/TooTouch/MemSeg/blob/main/%5Bexample%5D%20model%20overview.ipynb)

<p align='center'>
    <img width='1500' src='https://user-images.githubusercontent.com/37654013/198960086-fdbf39df-f680-4510-b94b-48341836f960.png'>
</p>


# Run

**Example**

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

## Demo

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

![](https://github.com/TooTouch/MemSeg/blob/main/assets/memseg.gif)

# Results

- **Backbone**: ResNet18

| 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}
}
```