This is a code implemention for paper "Self-Attention Autoencoder for Anomaly Segmentation"
The MVTec AD dataset is available at:
https://www.mvtec.com/company/research/datasets/mvtec-ad
We use pretrained ViT-B16 in the paper. You can get it at https://github.com/asyml/vision-transformer-pytorch.
python main.py --mode train --device cuda:0 --batch_size 32 --epochs 700
python main.py --mode evaluation
| Class | ROC-AUC | PRO-AUC |
|---|---|---|
| Carpet | 97.9 | 93.1 |
| Grid | 98.6 | 96.4 |
| Leather | 99.6 | 98.7 |
| Tile | 97.3 | 92.7 |
| Wood | 97.6 | 95.4 |
| Mean textures | 98.2 | 95.3 |
| Bottle | 97.9 | 94.3 |
| Cable | 96.8 | 89.0 |
| Capsule | 98.2 | 92.9 |
| Hazelnut | 98.5 | 96.6 |
| Metal Nut | 97.6 | 91.7 |
| Pill | 98.1 | 97.1 |
| Screw | 98.9 | 94.6 |
| Toothbrush | 98.7 | 93.1 |
| Transistor | 96.0 | 88.2 |
| Zipper | 96.9 | 90.3 |
| Mean textures | 97.8 | 92.8 |
| Mean | 97.9 | 93.6 |
The first row is input image, the second row is ground truth anomaly segmentation and the third is the anomaly score map produced by our model. The last row is the segmentation result when FPR of 0 for texture classes and FPR of 0.001 for object classes on corresponding training set are given.
https://github.com/YoungGod/DFR