Implementation of different Deep Learning Unsupervised Segmentation models in Pytorch (Lightning).
Asako Kanezaki. Unsupervised Image Segmentation by Backpropagation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. (pdf)
Implementation based on: https://github.com/kanezaki/pytorch-unsupervised-segmentation
Wonjik Kim*, Asako Kanezaki*, and Masayuki Tanaka. Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering. IEEE Transactions on Image Processing, accepted, 2020. (arXiv). *W. Kim and A. Kanezaki contributed equally to this work.
Implementation based on: https://github.com/kanezaki/pytorch-unsupervised-segmentation-tip
Xia, Xide, and Brian Kulis. "W-net: A Deep Model for Fully Unsupervised Image Segmentation." arXiv preprint arXiv:1711.08506 (2017).
Implementation based on: https://aswali.github.io/WNet/
Creation of the Environment
conda create -n hunan python=3.7.10
conda activate hunan
conda install pytorch==1.11.0 torchvision==0.12.0 cudatoolkit=11.3 -c pytorch
Installation of the Unsupervised-Segmentation library in editable mode
# Download the repository (git/github interface), for example: git clone https://github.com/fedric95/Unsupervised-Segmentation.git
cd Unsupervised-Segmentation
pip install -e .
Download of the Hunan-Baseline repository
cd ..
git clone https://github.com/fedric95/Hunan-Baseline.git
cd Hunan-Baseline
In this repository, in the examples directory, there is an example for each method that has been implemented.
ISB and DFC supports batch sizes grater than one but the computation is not efficient (it is not vectorized)