mrluin / Esfnet Pytorch
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ESFNet-Pytorch
Stars: ✭ 18
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python
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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
- set
root_dir
in./configs/config.cfg
, change the root_path like mentioned above. - set
divice_id
to choose which GPU will be used. - set
epochs
to control the length of the training phase. - 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~
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