The current code is designed for Windows, not for Linux.
Install (安装步骤):
conda create -n nnsam python=3.9
conda activate nnsamChoose a suitable Pytorch with CUDA to install
根据CUDA选择合适版本的Pytorch进行安装
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121pip install git+https://github.com/ChaoningZhang/MobileSAM.git
pip install timm
pip install git+https://github.com/Kent0n-Li/nnSAM.git
git clone https://github.com/Kent0n-Li/Medical-Image-Segmentation.git
cd Medical-Image-Segmentation-Benchmark
pip install -r requirements.txt运行可视化软件
python web.pyIf you only want to use nnSAM, please install this.
如果你只想运行nnSAM,请访问该代码仓库this
样例数据集：Demo Dataset
样例数据集：Demo Dataset
If you find this repository useful for your research, please use the following.
@article{li2023nnsam,
title={nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance},
author={Li, Yunxiang and Jing, Bowen and Li, Zihan and Wang, Jing and Zhang, You},
journal={arXiv preprint arXiv:2309.16967},
year={2023}
}
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