ROAD 🛣️ : The Radio Observatory Anomaly Detector

A repository containing the implementation of the paper entitled The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrograms

Installation

Install conda environment by:

    conda create --name road python=3.9.7

Run conda environment by:

    conda activate road

Install the appropriate pytorch version:

    conda install pytorch torchvision torchaudio pytorch-cuda=<VERSION> -c pytorch -c nvidia

Install dependancies by running:

    pip install -r requirements

Dataset

You will need to download the ROAD dataset and specify the its path using -data_path command line option.

Replication of results in paper

Run the following to replicate the results for the resnet34 used in the paper

    ./experiments/final_model.sh

or to run for all backbones

    ./experiments/test.sh

Alternatively the model weights can be downloaded and specified using the -model_name and -model_path flags.

Labelling with label-studio:

The labelling interface is based on label-studio. To get the label server running for the LOFAR_AD project, run the following:

  label-studio start LOFAR_AD --sampling uniform &

and

./webserver /home/mmesarcik/data/LOFAR/compressed/LOFAR_AD/LOFAR_AD_v1/ *.png files 8081

Licensing

Source code of ROAD is licensed under the MIT License.