ESFNet: Efficient Networks for Building Extraction from High-Resolution Images

The implementation of novel efficient neural network ESFNet

Clone the Repository

git clone https://github.com/mrluin/ESFNet-Pytorch.git

cd ./ESFNet-Pytorch

Installation using Conda

conda env create -f environment.yml

conda activate esfnet

Sample Dataset

For training, you can use as an example the WHU Building Datase.

You would need to download the cropped aerial images. The 3rd option

Directory Structure

Directory:
            #root | -- train 
                  | -- valid
                  | -- test
                  | -- save | -- {model.name} | -- datetime | -- ckpt-epoch{}.pth.format(epoch)
                            |                               | -- best_model.pth
                            |
                            | -- log | -- {model.name} | -- datetime | -- history.txt
                            | -- test| -- log | -- {model.name} | --datetime | -- history.txt
                                     | -- predict | -- {model.name} | --datetime | -- *.png

Training

1. set root_dir in ./configs/config.cfg, change the root_path like mentioned above.
2. set divice_id to choose which GPU will be used.
3. set epochs to control the length of the training phase.
4. setup the train.py script as follows:

python -m visdom.server -env_path='./visdom_log/' -port=8097 # start visdom server
python train.py

-env_path is where the visdom logfile store in, and -port is the port for visdom. You could also change the -port in train.py.

If my work give you some insights and hints, star me please! Thank you~