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PyTorch implementation of UNet++ (Nested U-Net).
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# PyTorch implementation of UNet++ (Nested U-Net)
[](LICENSE)
This repository contains code for a image segmentation model based on [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://arxiv.org/abs/1807.10165) implemented in PyTorch.
[**NEW**] Add support for multi-class segmentation dataset.
[**NEW**] Add support for PyTorch 1.x.
## Requirements
- PyTorch 1.x or 0.41
## Installation
1. Create an anaconda environment.
```sh
conda create -n=<env_name> python=3.6 anaconda
conda activate <env_name>
```
2. Install PyTorch.
```sh
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
```
3. Install pip packages.
```sh
pip install -r requirements.txt
```
## Training on [2018 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2018) dataset
1. Download dataset from [here](https://www.kaggle.com/c/data-science-bowl-2018/data) to inputs/ and unzip. The file structure is the following:
```
inputs
└── data-science-bowl-2018
├── stage1_train
| ├── 00ae65...
│ │ ├── images
│ │ │ └── 00ae65...
│ │ └── masks
│ │ └── 00ae65...
│ ├── ...
|
...
```
2. Preprocess.
```sh
python preprocess_dsb2018.py
```
3. Train the model.
```sh
python train.py --dataset dsb2018_96 --arch NestedUNet
```
4. Evaluate.
```sh
python val.py --name dsb2018_96_NestedUNet_woDS
```
### (Optional) Using LovaszHingeLoss
1. Clone LovaszSoftmax from [bermanmaxim/LovaszSoftmax](https://github.com/bermanmaxim/LovaszSoftmax).
```
git clone https://github.com/bermanmaxim/LovaszSoftmax.git
```
2. Train the model with LovaszHingeLoss.
```
python train.py --dataset dsb2018_96 --arch NestedUNet --loss LovaszHingeLoss
```
## Training on original dataset
Make sure to put the files as the following structure (e.g. the number of classes is 2):
```
inputs
└── <dataset name>
├── images
| ├── 0a7e06.jpg
│ ├── 0aab0a.jpg
│ ├── 0b1761.jpg
│ ├── ...
|
└── masks
├── 0
| ├── 0a7e06.png
| ├── 0aab0a.png
| ├── 0b1761.png
| ├── ...
|
└── 1
├── 0a7e06.png
├── 0aab0a.png
├── 0b1761.png
├── ...
```
1. Train the model.
```
python train.py --dataset <dataset name> --arch NestedUNet --img_ext .jpg --mask_ext .png
```
2. Evaluate.
```
python val.py --name <dataset name>_NestedUNet_woDS
```
## Results
### DSB2018 (96x96)
Here is the results on DSB2018 dataset (96x96) with LovaszHingeLoss.
| Model | IoU | Loss |
|:------------------------------- |:-------:|:-------:|
| U-Net | 0.839 | 0.365 |
| Nested U-Net | 0.842 |**0.354**|
| Nested U-Net w/ Deepsupervision |**0.843**| 0.362 |
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matched fp:84c6df25fe0f1d38, topic:image-segmentation, readme:image segmentation
matched fp:84c6df25fe0f1d38, topic:pytorch