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https://github.com/tootouch/memseg
Unofficial re-implementation of MemSeg for Anomaly Detection
https://github.com/tootouch/memseg
Last synced: 2 days ago
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Unofficial re-implementation of MemSeg for Anomaly Detection
- Host: GitHub
- URL: https://github.com/tootouch/memseg
- Owner: TooTouch
- License: mit
- Created: 2022-10-31T01:10:58.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-24T02:20:33.000Z (5 months ago)
- Last Synced: 2024-06-24T03:29:48.137Z (5 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 10.4 MB
- Stars: 169
- Watchers: 5
- Forks: 29
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
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) ]
## 2. Model Process
- [notebook](https://github.com/TooTouch/MemSeg/blob/main/%5Bexample%5D%20model%20overview.ipynb)
# Run
**Example**
```bash
python main.py configs=configs.yaml DATASET.target=bottle
```
## Demo
```
voila "[demo] model inference.ipynb" --port ${port} --Voila.ip ${ip}
```
# 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}
}
```