# MemSeg

**Repository Path**: chuanOO/MemSeg

## Basic Information

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

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-07
- **Last Updated**: 2023-12-07

## 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    |         98.31 |         99.05 |
| pill           |         96.21 |         88    |         90.23 |
| carpet         |         98.72 |         94.1  |         95.31 |
| hazelnut       |         97.89 |         89.28 |         94.86 |
| tile           |        100    |         98.97 |         98.84 |
| cable          |         83.71 |         74.69 |         73.21 |
| toothbrush     |        100    |         98.67 |         97.13 |
| transistor     |         92.42 |         75    |         79.41 |
| zipper         |         99.63 |         93.94 |         93    |
| metal_nut      |         90.42 |         80.99 |         90.62 |
| grid           |         99.92 |         96.48 |         95.87 |
| bottle         |        100    |         94.67 |         92.61 |
| capsule        |         92.34 |         83.45 |         84.34 |
| screw          |         81.64 |         83.04 |         82.93 |
| wood           |         99.74 |         94.9  |         94.45 |
| **Average**    |         95.51 |         89.63 |         90.79 |

# 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}
}
```