A highly configurable toolkit for training 3d/2d CNNs and general Neural Networks
ELEKTRONN is a highly configurable toolkit for training 3D/2D CNNs and general Neural Networks.
It is written in Python 2 and based on Theano, which allows CUDA-enabled GPUs to significantly accelerate the pipeline.
The package includes a sophisticated training pipeline designed for classification/localisation tasks on 3D/2D images. Additionally, the toolkit offers training routines for tasks on non-image data.
ELEKTRONN was created by Marius Killinger and Gregor Urban at the Max Planck Institute For Medical Research to solve connectomics tasks.
Membrane and mitochondria probability maps. Predicted with a CNN with recursive training. Data: zebra finch area X dataset j0126 by Jörgen Kornfeld.
$ elektronn-train MNIST_CNN_warp_config.py
This will download the MNIST data set and run a training defined in an example config file. The plots are saved to ~/CNN_Training/2D/MNIST_example_warp.
ELEKTRONN ├── doc # Documentation source files ├── elektronn │ ├── examples # Example scripts and config files │ ├── net # Neural network library code │ ├── scripts # Training script and profiling script │ ├── training # Training library code │ └── ... ├── LICENSE.rst ├── README.rst └── ...
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