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.

• 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.

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

Below is the implementation result of the test set ROCAUC on the MVTec AD dataset.