The goal of this repository is to provide an extendable toolbox for large-scale experimentation with deep learning techniques for image enhancement and segmentation of N-dimensional biological imaging data. It is designed to support high-performance computing clusters and utilize GPU-acceleration.
Training datasets, as well as model checkpoints used in the paper will be made accessible soon!
Run the following -->
conda create -n raygun python=3.9 tensorflow pytorch torchvision torchaudio cudatoolkit=11.3 affogato -c pytorch -c nvidia -c conda-forge
conda activate raygun
pip install git+https://github.com/htem/raygunShould you run into gcc / boost errors when conda/pip installing raygun, try installing libboost first:
sudo apt-get update
sudo apt-get install libboost-all-devraygun-train path/to/train_config.json