Awesome
Sub-Image Anomaly Detection with Deep Pyramid Correspondences (SPADE) in PyTorch
PyTorch implementation of Sub-Image Anomaly Detection with Deep Pyramid Correspondences (SPADE).
SPADE presents an anomaly segmentation approach which does not require a training stage.
It is fast, robust and achieves SOTA on MVTec AD
dataset.
- We used K=5 nearest neighbors, which differs from the original paper K=50.
Prerequisites
- python 3.6+
- PyTorch 1.5+
- sklearn, matplotlib
Install prerequisites with:
pip install -r requirements.txt
If you already download MVTec AD
dataset, move a file to data/mvtec_anomaly_detection.tar.xz
.
If you don't have a dataset file, it will be automatically downloaded during the code running.
Usage
To test SPADE on MVTec AD
dataset:
cd src
python main.py
After running the code above, you can see the ROCAUC results in src/result/roc_curve.png
Results
Below is the implementation result of the test set ROCAUC on the MVTec AD
dataset.
1. Image-level anomaly detection accuracy (ROCAUC %)
Paper | Implementation | |
---|---|---|
bottle | - | 97.2 |
cable | - | 84.8 |
capsule | - | 89.7 |
carpet | - | 92.8 |
grid | - | 47.3 |
hazelnut | - | 88.1 |
leather | - | 95.4 |
metal_nut | - | 71.0 |
pill | - | 80.1 |
screw | - | 66.7 |
tile | - | 96.5 |
toothbrush | - | 88.9 |
transistor | - | 90.3 |
wood | - | 95.8 |
zipper | - | 96.6 |
Average | 85.5 | 85.4 |
2. Pixel-level anomaly detection accuracy (ROCAUC %)
Paper | Implementation | |
---|---|---|
bottle | 98.4 | 97.0 |
cable | 97.2 | 92.3 |
capsule | 99.0 | 98.4 |
carpet | 97.5 | 98.9 |
grid | 93.7 | 98.3 |
hazelnut | 99.1 | 98.5 |
leather | 97.6 | 99.3 |
metal_nut | 98.1 | 97.1 |
pill | 96.5 | 95.0 |
screw | 98.9 | 99.1 |
tile | 87.4 | 92.8 |
toothbrush | 97.9 | 98.8 |
transistor | 94.1 | 86.6 |
wood | 88.5 | 95.3 |
zipper | 96.5 | 98.6 |
Average | 96.5 | 96.4 |
ROC Curve
Localization results