Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

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Deep Learning DoubleAE
Overview

Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Repository containing all scripts used in the studies of Online-compatible Unsupervised Non-resonant Anomaly Detection model.

To train your own model, first Download the official dataset from zenodo and use the example code to prepare the datasets. To run the training, use:

python AE40Mhz.py [--single/--double/--supervised/--all] [--load] --out NAME

To train a single AE, the double + decorrelatied method, supervisedd, or all of them respectively. Trained model weights are also providedd in the weights folder that can be loaded using the --load flag.

The output of the script will create an NAME.h5 file in the base directory. Use this file to plot the results using the script plot.py

python plot.py --file NAME.h5

Different plot options are available in the script.

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