| name | nnunet |
| description | No New U-Net — self-configuring framework for medical image segmentation. Automatically adapts to any dataset. Top performer on biomedical segmentation benchmarks (BraTS, KiTS, etc.). |
| tags | ["medical-imaging","segmentation","unet","deep-learning","pytorch","zorai"] |
Overview
nnUNet (No New U-Net) is a self-configuring framework for medical image segmentation that automatically adapts to any dataset. Consistently top-performing on benchmarks like BraTS, KiTS, and AMOS.
Installation
uv pip install nnunetv2
Plan and Preprocess
nnUNetv2_plan_and_preprocess -d DATASET_ID -pl nnUNetPlanner
Train
nnUNetv2_train DATASET_ID CONFIG 0
Inference
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c CONFIG
Python API
from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor
predictor = nnUNetPredictor()
predictor.initialize_from_trained_model_folder("nnUNet_results/DatasetXYZ", "3d_fullres")
predictor.predict_from_files("input_images", "output_segmentations")
Workflow
- Prepare dataset in nnUNet format (imagesTr, labelsTr, dataset.json)
- Run
nnUNetv2_plan_and_preprocess for automatic configuration
- Train with
nnUNetv2_train
- Predict with
nnUNetv2_predict or Python API
- Ensemble multiple configurations for best accuracy
