#2626th of 31 in Training and fine-tuningFor science, health and research
nnunet
Self-configuring U-Net framework for biomedical image segmentation
- Stars
- 9k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Apr 2024
- Language
- Python
8,145 on 1 Apr 20268,981 on 21 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
nnU-Net analyzes a training dataset, builds a fingerprint, and configures 2D and 3D U-Net variants with matching preprocessing, training, model selection and inference. It targets supervised semantic segmentation, mainly biomedical images, and doubles as a baseline for segmentation research. Installed with pip install nnunetv2 on top of PyTorch.
Who it is for: Researchers training segmentation models on medical or custom imaging data
Strengths
- Derives preprocessing and network configuration from a dataset fingerprint, with little manual tuning
- Covers the full workflow: preprocessing, training, model selection and inference
- Supports 2D and 3D data, arbitrary channel definitions and multiple image formats
- Documented residual encoder presets and finetuning from nnssl checkpoints
Weaknesses
- Supervised semantic segmentation only; not a general vision or LLM toolkit
- Requires setting up nnUNet_raw, nnUNet_preprocessed and nnUNet_results paths
- Last tagged release is 2024-04-21, though commits continue
- README does not state RAM or VRAM requirements
What it needs
- Needs PyTorch
- Models: U-Net (2D, 3D fullres, cascade), Residual encoder U-Net presets
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