Awesome
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
Introduction
Official implementation of the paper CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
Datasets
Folder Structure
├── CM-UNet (code)
├── data
│ ├── LoveDA
│ │ ├── Train
│ │ │ ├── Urban
│ │ │ │ ├── images_png (original images)
│ │ │ │ ├── masks_png (original masks)
│ │ │ │ ├── masks_png_convert (converted masks used for training)
│ │ │ │ ├── masks_png_convert_rgb (original rgb format masks)
│ │ │ ├── Rural
│ │ │ │ ├── images_png
│ │ │ │ ├── masks_png
│ │ │ │ ├── masks_png_convert
│ │ │ │ ├── masks_png_convert_rgb
│ │ ├── Val (the same with Train)
│ ├── vaihingen
│ │ ├── train_images (original)
│ │ ├── train_masks (original)
│ │ ├── test_images (original)
│ │ ├── test_masks (original)
│ │ ├── test_masks_eroded (original)
│ │ ├── train (processed)
│ │ ├── test (processed)
│ ├── potsdam (the same with vaihingen)
Install
conda create -n RS python==3.8
conda install cudatoolkit==11.8 -c nvidia
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
conda install -c "nvidia/label/cuda-11.8.0" cuda-nvcc
conda install packaging
cd CM-UNet
pip install -r requirements.txt
cd geoseg/Mamba-UNet
cd mamba
python setup.py install
cd ../ ; cd causal-conv1d
python setup.py install
RUN
bash run_loveda.sh ${GPUID}
Results
- Framework (CM-UNet)
- Results on Potsdam
- Results on Vaihingen
- Results on LoveDA
Acknowledgement
Many thanks the following projects's contributions to CM-UNet.