Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection in PyTorch
PyTorch implementation of Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection.
This paper presents an anomaly detection approach that consists of fitting a multivariate Gaussian to normal data in the pre-trained deep feature representations, using Mahalanobis distance as anomaly score.
It is a simple yet effective approach and achieves SOTA on MVTec AD
dataset.
- python 3.6+
- PyTorch 1.5+
- efficientnet_pytorch == 0.6.3
- 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 this implementation code 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_{model_name}.png
Below is the implementation result of the test set ROCAUC on the MVTec AD
dataset.
Paper | Implementation | |
---|---|---|
bottle | - | 100.0 |
cable | - | 94.2 |
capsule | - | 92.3 |
carpet | - | 98.1 |
grid | - | 94.6 |
hazelnut | - | 98.6 |
leather | - | 100.0 |
metal_nut | - | 94.3 |
pill | - | 83.4 |
screw | - | 78.1 |
tile | - | 98.6 |
toothbrush | - | 96.7 |
transistor | - | 96.1 |
wood | - | 98.5 |
zipper | - | 97.7 |
Average | 94.8 | 94.7 |