elektronn3

A PyTorch-based library for research on convolutional neural networks for 3D semantic segmentation. Its focus is on HDF5 data loading/augmentation, training, monitoring and model evaluation.

elektronn3 is under active development and its API is not yet stable.

For a roadmap of planned features, see the "enhancement" issues on the tracker.

Requirements

Linux (support for Windows, MacOS and other systems is not planned)
Python 3.6 or later
PyTorch 1.0
For other requirements see requirements.txt

Setup

Ensure that all of the requirements listed above are installed. We recommend using conda or a virtualenv for that. To install elektronn3 in development mode, run

git clone https://github.com/ELEKTRONN/elektronn3 elektronn3-dev
pip install -e elektronn3-dev

To update your installation, just git pull in your clone directory.

Training

For a quick test run, first ensure that the neuro_data_cdhw data set is in the expected path:

wget https://github.com/ELEKTRONN/elektronn.github.io/releases/download/neuro_data_cdhw/neuro_data_cdhw.zip
unzip neuro_data_cdhw.zip -d ~/neuro_data_cdhw

To test training with our custom U-Net-derived architecture in elektronn3, you can run:

python3 train_unet_neurodata.py

Training shell

Hitting Ctrl-C anytime during the training will drop you to the IPython training shell where you can access training data and make interactive changes.
To continue training, hit Ctrl-D twice.
If you want the process to terminate after leaving the shell, set self.terminate = True inside it and then hit Ctrl-D twice.

Using Tensorboard

Tensorboard logs are saved in ~/e3training/ by default, so you can track training progress by running a tensorboard server there:

tensorboard --logdir ~/e3training/

Then you can view the visualizations at http://localhost:6006.

Contributors

The elektronn3 project is being developed by the ELEKTRONN team at the Max Planck Institute of Neurobiology and is funded by Winfried Denk's lab.

Jörgen Kornfeld is academic advisor to this project.