https://github.com/LeeJunHyun/Image_Segmentation

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.
https://github.com/LeeJunHyun/Image_Segmentation

Last synced: about 1 month ago
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Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

• Host: GitHub
• URL: https://github.com/LeeJunHyun/Image_Segmentation
• Owner: LeeJunHyun
• Created: 2018-06-18T08:27:27.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2023-06-30T05:20:58.000Z (almost 2 years ago)
• Last Synced: 2025-03-24T00:19:59.177Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 259 KB
• Stars: 2,862
• Watchers: 23
• Forks: 614
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-computer-vision-papers - U-Net and its variant code
• awesome-healthmetrics - Image Segmentation with Pytorch

README

        
### pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net

**(This repository is no longer being updated)**

**U-Net: Convolutional Networks for Biomedical Image Segmentation**

https://arxiv.org/abs/1505.04597

**Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation**

https://arxiv.org/abs/1802.06955

**Attention U-Net: Learning Where to Look for the Pancreas**

https://arxiv.org/abs/1804.03999

**Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net)**

## U-Net

![U-Net](/img/U-Net.png)

## R2U-Net

![R2U-Net](/img/R2U-Net.png)

## Attention U-Net

![AttU-Net](/img/AttU-Net.png)

## Attention R2U-Net

![AttR2U-Net](/img/AttR2U-Net.png)

## Evaluation

we just test the models with [ISIC 2018 dataset](https://challenge2018.isic-archive.com/task1/training/). The dataset was split into three subsets, training set, validation set, and test set, which the proportion is 70%, 10% and 20% of the whole dataset, respectively. The entire dataset contains 2594 images where 1815 images were used

for training, 259 for validation and 520 for testing models.

![evaluation](/img/Evaluation.png)