Awesome
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
Our entire code is built based on nnUNet, and you can follow the nnUNet instructions exactly.
Install nnSAM depending on your use case:
conda create -n nnsam python=3.9
conda activate nnsam
pip install git+https://github.com/ChaoningZhang/MobileSAM.git
pip install timm
pip install git+https://github.com/Kent0n-Li/nnSAM.git
It is important to input "set MODEL_NAME=nnsam" before using it.
set MODEL_NAME=nnsam
nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity
nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD [additional options, see -h]
nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD --val --npz
nnUNetv2_train DATASET_NAME_OR_ID 2d FOLD
nnUNetv2_train DATASET_NAME_OR_ID 3d_fullres FOLD
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_NAME_OR_ID -c CONFIGURATION --save_probabilities
How to get started?
Read these:
Additional information:
- Region-based training
- Manual data splits
- Pretraining and finetuning
- Intensity Normalization in nnU-Net
- Manually editing nnU-Net configurations
- Extending nnU-Net
- What is different in V2?
Acknowledgements
nnU-Net is developed and maintained by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ).