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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 (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-30T05:20:58.000Z (over 1 year ago)
- Last Synced: 2024-10-29T17:49:24.614Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 259 KB
- Stars: 2,718
- Watchers: 25
- Forks: 600
- Open Issues: 2
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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**(This repository is no longer being updated)**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
## R2U-Net
## Attention U-Net
## Attention R2U-Net
## 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.
