The implementation of novel efficient neural network ESFNet

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

cd ./ESFNet-Pytorch 

conda env create -f environment.yml 

conda activate esfnet 

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: #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 

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~