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https://github.com/abc-125/segad
Segmentation-based Anomaly Detector (SegAD)
https://github.com/abc-125/segad
anomaly-detection anomalydetection
Last synced: about 2 months ago
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Segmentation-based Anomaly Detector (SegAD)
- Host: GitHub
- URL: https://github.com/abc-125/segad
- Owner: abc-125
- License: mit
- Created: 2024-03-03T09:38:11.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-05-14T15:19:18.000Z (9 months ago)
- Last Synced: 2024-05-15T01:21:49.042Z (9 months ago)
- Topics: anomaly-detection, anomalydetection
- Language: Python
- Homepage:
- Size: 24.4 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# Supervised Anomaly Detection for Complex Industrial Images
Official code for our CVPR 2024 [paper](https://arxiv.org/abs/2405.04953)
[VAD repository](https://github.com/abc-125/vad)
## Get Started
### Enviroment
```bash
pip install -r requirements.txt
```
### Data
1. Download [segmentation maps for VisA](https://drive.google.com/file/d/1ZVMxtb6PY958qigxAQcLEifWsRdnLaI4/view?usp=sharing).
2. Download [anomaly maps for EfficientAD](https://drive.google.com/file/d/1mknzBIE6Heqfr5_BQIFOojzuPDQG2o_O/view?usp=sharing).
3. Download [anomaly maps for RD4AD](https://drive.google.com/file/d/1Pap5-8x74_AROFRxjcBvIu9XdqvzHMs8/view?usp=sharing).
4. Data structure should look as following:
```shell
data
|-- visa_segm
|-- anomaly_maps
|-----|--efficient_ad
|-----|--rd4ad
```
### Train and evaluate
Only VisA dataset is available for now. List of available models: `["efficient_ad", "rd4ad", "all_ad"]`. `"all_ad"` includes both EfficientAD and RD4AD.
```bash
python main.py --model efficient_ad
```
## Results
Cl. AUROC (image-level) for SegAD with different sources of anomaly maps.
| model | mean | candle | capsules | cashew | chewinggum | fryum | macaroni1 | macaroni2 | pcb1 | pcb2 | pcb3 | pcb4 | pipe_fryum |
| -------------------| :----: | :----: | :-------: | :----: | :---------: | :----: | :-------: | :-------: | :---:| :---:| :---:| :---:| :--------: |
| RD4AD + SegAD | 95.3 | 98.5 | 80.2 | 98.9 | 99.4 | 96.1 | 97.4 | 90.7 | 96.4 | 96.3 | 94.1 | 99.9 | 95.8 |
| EfficientAD + SegAD| 98.3 | 98.7 | 89.7 | 98.6 | 99.9 | 98.6 | 99.5 | 98.1 | 99.5 | 99.7 | 98.4 | 99.3 | 99.2 |
| All AD + SegAD | 98.4 | 99.0 | 90.7 | 99.0 | 99.9 | 98.5 | 99.4 | 98.1 | 99.2 | 99.7 | 98.3 | 99.8 | 99.1 |
## Acknowledgement
We use [EfficientAD](https://github.com/nelson1425/EfficientAD) and [Anomalib](https://github.com/openvinotoolkit/anomalib/tree/main) for baseline anomaly detection models. We are thankful for their amazing work!
## Citation
Please cite this paper if it helps your project:
```
@inproceedings{baitieva2024supervised,
title={Supervised Anomaly Detection for Complex Industrial Images},
author={Aimira Baitieva and David Hurych and Victor Besnier and Olivier Bernard},
booktitle={CVPR},
year={2024}
}
```