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.

Prerequisites

• 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.

Usage

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

Results

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

ROC Curve