加入CODE CHINA

· 不限速    · 不限空间    · 不限人数    · 私仓免费

免费加入
README.md

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

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

R2U-Net

R2U-Net

Attention U-Net

AttU-Net

Attention R2U-Net

AttR2U-Net

Evaluation

we just test the models with ISIC 2018 dataset. 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

项目简介

🚀 Github 镜像仓库 🚀

源项目地址

https://github.com/leejunhyun/image_segmentation

发行版本

当前项目没有发行版本

贡献者 1

L leejunhyun @leejunhyun

开发语言

  • Python 99.4 %
  • Shell 0.6 %