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deep-clustering-toolbox

PyTorch Vision toolbox not only for deep-clustering

Introduction

This repo contains the base code for a deep learning framework using PyTorch, to benchmark algorithms for various dataset. The current version supports MNIST, CIFAR10, SVHN and STL-10 for semisupervised and unsupervised learning. ACDC, Promise12, WMH and so on are supported as segmentation counterpart.

Features:

  • Powerful cmd parser using yaml module, providing flexible input formats without predefined argparser.
  • Automatic checkpoint management adapting to various settings
  • Automatic meter recording and experimental status plotting using matplotlib and threads
  • Various build-in loss functions and help tricks and assert statements frequently used in PyTorch Framework, such as disable_tracking_bn, ema, vat, etc.
  • Various post-processing tools such as Viewer for Medical image segmentations, multislice_viwers for 3D dataset real-time debug and report script for experimental summaries.
  • Extendable modules for rapid development.

Several projects are benefited from this scalable framework, builing top on this, including:

  • DeepClustering implemented for
  • SemiSupervised classification for
  • Semi-Supervised Learning by Augmented Distribution Alignment,
  • Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning,
  • Temporal Ensembling for Semi-Supervised Learning,
  • Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
  • SemiSupervised Segmentation for
  • Discretely-constrained CNN for

They are examples how to develop research framework with the assistance of our proposed deep-clustering-toolbox.


Playground

Several papers have been implemented based on this framework. I store them in the playground folder. The papers include:


Installation

git clone https://github.com/jizongFox/deep-clustering-toolbox.git
cd deep-clustering-toolbox  
python setup install # for those who do not want to make changes immediately.
# or
python setup develop # for those who want to modify the code and make the impact immediate.

Or very simply

pip install deepclustering

Citation

If you feel useful for your project, please consider citing this work.

@article{peng2019deep,
  title={Deep Co-Training for Semi-Supervised Image Segmentation},
  author={Peng, Jizong and Estradab, Guillermo and Pedersoli, Marco and Desrosiers, Christian},
  journal={arXiv preprint arXiv:1903.11233},
  year={2019}
}

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0.0.2

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