U-net

A simple pytorch implementation of U-net, as described in the paper: https://arxiv.org/abs/1505.04597

This project is meant to be a dead-simple implementation of the model.
The only dependencies are pytorch, numpy and pillow.

The main differences with the paper are:

• no padding in the pooling, which makes handling dimensions easier
• no weight balancing in the softmax to deal with class inbalance

Example dataset

The example dataset is from the ISBI Challenge. More information here: http://brainiac2.mit.edu/isbi_challenge/.

A few outputs from the test dataset, after 300 iterations:

Download and put the files in the data directory. It should be like this:

data
├── test-volume.tif
├── train-labels.tif
└── train-volume.tif

Installation

pip install torch numpy pillow
mkdir model

Training

python train.py

Prediction on test dataset

python predict.py

Bibliography:

• https://github.com/milesial/Pytorch-UNet