UltraLight VM-UNet
Support single gpu training
The environment
conda create -n vmunet python=3.8
conda activate vmunet
pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
pip install packaging
pip install timm==0.4.12
pip install pytest chardet yacs termcolor
pip install submitit tensorboardX
pip install triton==2.0.0
pip install causal_conv1d==1.0.0 # causal_conv1d-1.0.0+cu118torch1.13cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
pip install mamba_ssm==1.0.1 # mmamba_ssm-1.0.1+cu118torch1.13cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
pip install scikit-learn matplotlib thop h5py SimpleITK scikit-image medpy yacs
1.The file format reference is as follows. (The image is a 24-bit jpg image. The mask is an 8-bit png image. (0 pixel dots for background, 255 pixel dots for target))
datasets/
├── train
│ ├── images
│ └── masks
└── val
├── images
└── masks
2.config_setting.visual_imgs=True,wirte 10 images to the folder and ensure that the label after data augmentation is correct. Data augmentation may cause label errors, so check it!
python train.py
python inference.py
[UltraLight-VM-UNet](https://github.com/wurenkai/UltraLight-VM-UNet)
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