LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong
This is an initial benchmark for Unsupervised Domain Adaptation.
Getting Started
Requirements:
- pytorch >= 1.7.0
- python >=3.6
- pandas >= 1.1.5
Prepare LoveDA Dataset
ln -s </path/to/LoveDA> ./LoveDA
Evaluate CBST Model on the predict set
1. Download the pre-trained weights
2. Move weight file to log directory
mkdir -vp ./log/
mv ./CBST_2Urban.pth ./log/CBST_2Urban.pth
3. Evaluate on Urban test set
bash ./scripts/predict_cbst.sh
Submit your test results on LoveDA Unsupervised Domain Adaptation Challenge and you will get your Test score.
Train CBST Model
From Rural to Urban
bash ./scripts/train_cbst.sh
Eval CBST Model on Urban val set
bash ./scripts/eval_cbst.sh