Road_extraction_network

This is a Road_extraction_network, it can be easy to read and learn. It also can add your network and dataset to train.

0. Quick start

1. Git clone from GitHub.

git clone https://github.com/ShanZard/Road_extraction_network

2. cd to Semantic-segmentation-framework

cd Semantic-segmentation-framework

3. install requirements

pip install -r requirements

1. Dataset

1. Should organize dataset like this:

├── data
│   ├── your_dataset
|   |   |——train
|   |   |   ├── images
│   │   │   │   ├── xxx{img_suffix}
│   │   │   │   ├── yyy{img_suffix}
│   │   │   │   ├── zzz{img_suffix}
│   │   │   ├── mask
│   │   │   │   ├── xxx{seg_map_suffix}
│   │   │   │   ├── yyy{seg_map_suffix}
│   │   │   │   ├── zzz{seg_map_suffix}
│   │   ├── val/test
│   │   │   ├── images
│   │   │   │   ├── xxx{img_suffix}
│   │   │   │   ├── yyy{img_suffix}
│   │   │   │   ├── zzz{img_suffix}
│   │   │   ├── mask
│   │   │   │   ├── xxx{seg_map_suffix}
│   │   │   │   ├── yyy{seg_map_suffix}
│   │   │   │   ├── zzz{seg_map_suffix}

Note:

1. images names should be same as mask
2. mask should be [h,w], if your mask is RGB[3,h,w], you can use tools/pre_processdataset.py to convert it .

2. Before train

1. should change utils/palette.py
2. change your args in train.py

3. Test

We will update the complete code as soon as possible