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[2020_10_21] Update: We now have documentation for common questions and common issues. We now also provide reference epoch times for several datasets and tips on how to identify bottlenecks.

Please read these documents before opening a new issue!


In 3D biomedical image segmentation, dataset properties like imaging modality, image sizes, voxel spacings, class ratios etc vary drastically. For example, images in the Liver and Liver Tumor Segmentation Challenge dataset are computed tomography (CT) scans, about 512x512x512 voxels large, have isotropic voxel spacings and their intensity values are quantitative (Hounsfield Units). The Automated Cardiac Diagnosis Challenge dataset on the other hand shows cardiac structures in cine MRI with a typical image shape of 10x320x320 voxels, highly anisotropic voxel spacings and qualitative intensity values. In addition, the ACDC dataset suffers from slice misalignments and a heterogeneity of out-of-plane spacings which can cause severe interpolation artifacts if not handled properly.

In current research practice, segmentation pipelines are designed manually and with one specific dataset in mind. Hereby, many pipeline settings depend directly or indirectly on the properties of the dataset and display a complex co-dependence: image size, for example, affects the patch size, which in turn affects the required receptive field of the network, a factor that itself influences several other hyperparameters in the pipeline. As a result, pipelines that were developed on one (type of) dataset are inherently incomaptible with other datasets in the domain.

nnU-Net is the first segmentation method that is designed to deal with the dataset diversity found in the domain. It condenses and automates the keys decisions for designing a successful segmentation pipeline for any given dataset.

nnU-Net makes the following contributions to the field:

  1. Standardized baseline: nnU-Net is the first standardized deep learning benchmark in biomedical segmentation. Without manual effort, researchers can compare their algorithms against nnU-Net on an arbitrary number of datasets to provide meaningful evidence for proposed improvements.
  2. Out-of-the-box segmentation method: nnU-Net is the first plug-and-play tool for state-of-the-art biomedical segmentation. Inexperienced users can use nnU-Net out of the box for their custom 3D segmentation problem without need for manual intervention.
  3. Framework: nnU-Net is a framework for fast and effective development of segmentation methods. Due to its modular structure, new architectures and methods can easily be integrated into nnU-Net. Researchers can then benefit from its generic nature to roll out and evaluate their modifications on an arbitrary number of datasets in a standardized environment.

For more information about nnU-Net, please read the following paper:

Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method 
for deep learning-based biomedical image segmentation. Nature Methods, 1-9.

Please also cite this paper if you are using nnU-Net for your research!

Table of Contents


nnU-Net has been tested on Linux (Ubuntu 16, 18 and 20; centOS, RHEL). We do not provide support for other operating systems.

nnU-Net requires a GPU! For inference, the GPU should have 4 GB of VRAM. For training nnU-Net models the GPU should have at least 10 GB (popular non-datacenter options are the RTX 2080ti, RTX 3080 or RTX 3090). Due to the use of automated mixed precision, fastest training times are achieved with the Volta architecture (Titan V, V100 GPUs) when installing pytorch the easy way. Since pytorch comes with cuDNN 7.6.5 and tensor core acceleration on Turing GPUs is not supported for 3D convolutions in this version, you will not get the best training speeds on Turing GPUs. You can remedy that by compiling pytorch from source (see here) using cuDNN 8.0.2 or newer. This will unlock Turing GPUs (RTX 2080ti, RTX 6000) for automated mixed precision training with 3D convolutions and make the training blistering fast as well. Note that future versions of pytorch may include cuDNN 8.0.2 or newer by default and compiling from source will not be necessary. We don't know the speed of Ampere GPUs with vanilla vs self-compiled pytorch yet - this section will be updated as soon as we know.

For training, we recommend a strong CPU to go along with the GPU. At least 6 CPU cores (12 threads) are recommended. CPU requirements are mostly related to data augmentation and scale with the number of input channels. They are thus higher for datasets like BraTS which use 4 image modalities and lower for datasets like LiTS which only uses CT images.

We very strongly recommend you install nnU-Net in a virtual environment. Here is a quick how-to for Ubuntu. If you choose to compile pytorch from source, you will need to use conda instead of pip. In that case, please set the environment variable OMP_NUM_THREADS=1 (preferably in your bashrc using export OMP_NUM_THREADS=1). This is important!

Python 2 is deprecated and not supported. Please make sure you are using Python 3.

  1. Install PyTorch. You need at least version 1.6
  2. Install nnU-Net depending on your use case:
    1. For use as standardized baseline, out-of-the-box segmentation algorithm or for running inference with pretrained models:

      pip install nnunet

    2. For use as integrative framework (this will create a copy of the nnU-Net code on your computer so that you can modify it as needed):

      git clone https://github.com/MIC-DKFZ/nnUNet.git
      cd nnUNet
      pip install -e .
  3. nnU-Net needs to know where you intend to save raw data, preprocessed data and trained models. For this you need to set a few of environment variables. Please follow the instructions here.
  4. (OPTIONAL) Install hiddenlayer. hiddenlayer enables nnU-net to generate plots of the network topologies it generates (see Model training). To install hiddenlayer, run the following commands:
    pip install --upgrade git+https://github.com/nanohanno/hiddenlayer.git@bugfix/get_trace_graph#egg=hiddenlayer

Installing nnU-Net will add several new commands to your terminal. These commands are used to run the entire nnU-Net pipeline. You can execute them from any location on your system. All nnU-Net commands have the prefix nnUNet_ for easy identification.

Note that these commands simply execute python scripts. If you installed nnU-Net in a virtual environment, this environment must be activated when executing the commands.

All nnU-Net commands have a -h option which gives information on how to use them.

A typical installation of nnU-Net can be completed in less than 5 minutes. If pytorch needs to be compiled from source (which is what we currently recommend when using Turing GPUs), this can extend to more than an hour.


To familiarize yourself with nnU-Net we recommend you have a look at the Examples before you start with your own dataset.

How to run nnU-Net on a new dataset

Given some dataset, nnU-Net fully automatically configures an entire segmentation pipeline that matches its properties. nnU-Net covers the entire pipeline, from preprocessing to model configuration, model training, postprocessing all the way to ensembling. After running nnU-Net, the trained model(s) can be applied to the test cases for inference.

Dataset conversion

nnU-Net expects datasets in a structured format. This format closely (but not entirely) follows the data structure of the Medical Segmentation Decthlon. Please read this for information on how to convert datasets to be compatible with nnU-Net.

Experiment planning and preprocessing

As a first step, nnU-Net extracts a dataset fingerprint (a set of dataset-specific properties such as image sizes, voxel spacings, intensity information etc). This information is used to create three U-Net configurations: a 2D U-Net, a 3D U-Net that operated on full resolution images as well as a 3D U-Net cascade where the first U-Net creates a coarse segmentation map in downsampled images which is then refined by the second U-Net.

Provided that the requested raw dataset is located in the correct folder (nnUNet_raw_data_base/nnUNet_raw_data/TaskXXX_MYTASK, also see here), you can run this step with the following command:

nnUNet_plan_and_preprocess -t XXX --verify_dataset_integrity

XXX is the integer identifier associated with your Task name TaskXXX_MYTASK. You can pass several task IDs at once.

Running nnUNet_plan_and_preprocess will populate your folder with preprocessed data. You will find the output in nnUNet_preprocessed/TaskXXX_MYTASK. nnUNet_plan_and_preprocess creates subfolders with preprocessed data for the 2D U-Net as well as all applicable 3D U-Nets. It will also create 'plans' files (with the ending.pkl) for the 2D and 3D configurations. These files contain the generated segmentation pipeline configuration and will be read by the nnUNetTrainer (see below). Note that the preprocessed data folder only contains the training cases. The test images are not preprocessed (they are not looked at at all!). Their preprocessing happens on the fly during inference.

--verify_dataset_integrity should be run at least for the first time the command is run on a given dataset. This will execute some checks on the dataset to ensure that it is compatible with nnU-Net. If this check has passed once, it can be omitted in future runs. If you adhere to the dataset conversion guide (see above) then this should pass without issues :-)

Note that nnUNet_plan_and_preprocess accepts several additional input arguments. Running -h will list all of them along with a description. If you run out of RAM during preprocessing, you may want to adapt the number of processes used with the -tl and -tf options.

After nnUNet_plan_and_preprocess is completed, the U-Net configurations have been created and a preprocessed copy of the data will be located at nnUNet_preprocessed/TaskXXX_MYTASK.

Extraction of the dataset fingerprint can take from a couple of seconds to several minutes depending on the properties of the segmentation task. Pipeline configuration given the extracted finger print is nearly instantaneous (couple of seconds). Preprocessing depends on image size and how powerful the CPU is. It can take between seconds and several tens of minutes.

Model training

nnU-Net trains all U-Net configurations in a 5-fold cross-validation. This enables nnU-Net to determine the postprocessing and ensembling (see next step) on the training dataset. Per default, all U-Net configurations need to be run on a given dataset. There are, however situations in which only some configurations (and maybe even without running the cross-validation) are desired. See FAQ for more information.

Note that not all U-Net configurations are created for all datasets. In datasets with small image sizes, the U-Net cascade is omitted because the patch size of the full resolution U-Net already covers a large part of the input images.

Training models is done with the nnUNet_train command. The general structure of the command is:

nnUNet_train CONFIGURATION TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD  --npz (additional options)

CONFIGURATION is a string that identifies the requested U-Net configuration. TRAINER_CLASS_NAME is the name of the model trainer. If you implement custom trainers (nnU-Net as a framework) you can specify your custom trainer here. TASK_NAME_OR_ID specifies what dataset should be trained on and FOLD specifies which fold of the 5-fold-cross-validaton is trained.

nnU-Net stores a checkpoint every 50 epochs. If you need to continue a previous training, just add a -c to the training command.

IMPORTANT: --npz makes the models save the softmax outputs during the final validation. It should only be used for trainings where you plan to run nnUNet_find_best_configuration afterwards (this is nnU-Nets automated selection of the best performing (ensemble of) configuration(s), see below). If you are developing new trainer classes you may not need the softmax predictions and should therefore omit the --npz flag. Exported softmax predictions are very large and therefore can take up a lot of disk space. If you ran initially without the --npz flag but now require the softmax predictions, simply run


to generate them. This will only rerun the validation, not the training.

See nnUNet_train -h for additional options.

2D U-Net

For FOLD in [0, 1, 2, 3, 4], run:

nnUNet_train 2d nnUNetTrainerV2 TaskXXX_MYTASK FOLD --npz

3D full resolution U-Net

For FOLD in [0, 1, 2, 3, 4], run:

nnUNet_train 3d_fullres nnUNetTrainerV2 TaskXXX_MYTASK FOLD --npz

3D U-Net cascade

3D low resolution U-Net

For FOLD in [0, 1, 2, 3, 4], run:

nnUNet_train 3d_lowres nnUNetTrainerV2 TaskXXX_MYTASK FOLD --npz
3D full resolution U-Net

For FOLD in [0, 1, 2, 3, 4], run:

nnUNet_train 3d_cascade_fullres nnUNetTrainerV2CascadeFullRes TaskXXX_MYTASK FOLD --npz

Note that the 3D full resolution U-Net of the cascade requires the five folds of the low resolution U-Net to be completed beforehand!

The trained models will we written to the RESULTS_FOLDER/nnUNet folder. Each training obtains an automatically generated output folder name:


For Task002_Heart (from the MSD), for example, this looks like this:

├── 2d
│   └── Task02_Heart
│       └── nnUNetTrainerV2__nnUNetPlansv2.1
│           ├── fold_0
│           ├── fold_1
│           ├── fold_2
│           ├── fold_3
│           └── fold_4
├── 3d_cascade_fullres
├── 3d_fullres
│   └── Task02_Heart
│       └── nnUNetTrainerV2__nnUNetPlansv2.1
│           ├── fold_0
│           │   ├── debug.json
│           │   ├── model_best.model
│           │   ├── model_best.model.pkl
│           │   ├── model_final_checkpoint.model
│           │   ├── model_final_checkpoint.model.pkl
│           │   ├── network_architecture.pdf
│           │   ├── progress.png
│           │   └── validation_raw
│           │       ├── la_007.nii.gz
│           │       ├── la_007.pkl
│           │       ├── la_016.nii.gz
│           │       ├── la_016.pkl
│           │       ├── la_021.nii.gz
│           │       ├── la_021.pkl
│           │       ├── la_024.nii.gz
│           │       ├── la_024.pkl
│           │       ├── summary.json
│           │       └── validation_args.json
│           ├── fold_1
│           ├── fold_2
│           ├── fold_3
│           └── fold_4
└── 3d_lowres

Note that 3d_lowres and 3d_cascade_fullres are not populated because this dataset did not trigger the cascade. In each model training output folder (each of the fold_x folder, 10 in total here), the following files will be created (only shown for one folder above for brevity):

  • debug.json: Contains a summary of blueprint and inferred parameters used for training this model. Not easy to read, but very useful for debugging ;-)
  • model_best.model / model_best.model.pkl: checkpoint files of the best model identified during training. Not used right now.
  • model_final_checkpoint.model / model_final_checkpoint.model.pkl: checkpoint files of the final model (after training has ended). This is what is used for both validation and inference.
  • network_architecture.pdf (only if hiddenlayer is installed!): a pdf document with a figure of the network architecture in it.
  • progress.png: A plot of the training (blue) and validation (red) loss during training. Also shows an approximation of the evlauation metric (green). This approximation is the average Dice score of the foreground classes. It should, however, only to be taken with a grain of salt because it is computed on randomly drawn patches from the validation data at the end of each epoch, and the aggregation of TP, FP and FN for the Dice computation treats the patches as if they all originate from the same volume ('global Dice'; we do not compute a Dice for each validation case and then average over all cases but pretend that there is only one validation case from which we sample patches). The reason for this is that the 'global Dice' is easy to compute during training and is still quite useful to evaluate whether a model is training at all or not. A proper validation is run at the end of the training.
  • validation_raw: in this folder are the predicted validation cases after the training has finished. The summary.json contains the validation metrics (a mean over all cases is provided at the end of the file).

During training it is often useful to watch the progress. We therefore recommend that you have a look at the generated progress.png when running the first training. It will be updated after each epoch.

Training times largely depend on the GPU. The smallest GPU we recommend for training is the Nvidia RTX 2080ti. With this GPU (and pytorch compiled with cuDNN 8.0.2), all network trainings take less than 2 days.

Multi GPU training

Multi GPU training is experimental and NOT RECOMMENDED!

nnU-Net supports two different multi-GPU implementation: DataParallel (DP) and Distributed Data Parallel (DDP) (but currently only on one host!). DDP is faster than DP and should be preferred if possible. However, if you did not install nnunet as a framework (meaning you used the pip install nnunet variant), DDP is not available. It requires a different way of calling the correct python script (see below) which we cannot support from our terminal commands.

Distributed training currently only works for the basic trainers (2D, 3D full resolution and 3D low resolution) and not for the second, high resolution U-Net of the cascade. The reason for this is that distributed training requires some changes to the network and loss function, requiring a new nnUNet trainer class. This is, as of now, simply not implemented for the cascade, but may be added in the future.

To run distributed training (DP), use the following command:


Note that nnUNetTrainerV2 was replaced with nnUNetTrainerV2_DP. Just like before, CONFIGURATION can be 2d, 3d_lowres or 3d_fullres. TASK_NAME_OR_ID refers to the task you would like to train and FOLD is the fold of the cross-validation. GPUS (integer value) specifies the number of GPUs you wish to train on. To specify which GPUs you want to use, please make use of the CUDA_VISIBLE_DEVICES envorinment variable to specify the GPU ids (specify as many as you configure with -gpus GPUS). --dbs, if set, will distribute the batch size across GPUs. So if nnUNet configures a batch size of 2 and you run on 2 GPUs , each GPU will run with a batch size of 1. If you omit --dbs, each GPU will run with the full batch size (2 for each GPU in this example for a total of batch size 4).

To run the DDP training you must have nnU-Net installed as a framework. Your current working directory must be the nnunet folder (the one that has the dataset_conversion, evaluation, experiment_planning, ... subfolders!). You can then run the DDP training with the following command:

CUDA_VISIBLE_DEVICES=0,1,2... python -m torch.distributed.launch --master_port=XXXX --nproc_per_node=Y run/run_training_DDP.py CONFIGURATION nnUNetTrainerV2_DDP TASK_NAME_OR_ID FOLD --dbs

XXXX must be an open port for process-process communication (something like 4321 will do on most systems). Y is the number of GPUs you wish to use. Remember that we do not (yet) support distributed training across compute nodes. This all happens on the same system. Again, you can use CUDA_VISIBLE_DEVICES=0,1,2 to control what GPUs are used. If you run more than one DDP training on the same system (say you have 4 GPUs and you run two training with 2 GPUs each) you need to specify a different --master_port for each training!

IMPORTANT! Multi-GPU training results in models that cannot be used for inference easily (as said above, all of this is experimental ;-) ). After finishing the training of all folds, run nnUNet_change_trainer_class on the folder where the trained model is (see nnUNet_change_trainer_class -h for instructions). After that you can run inference.

Identifying the best U-Net configuration

Once all models are trained, use the following command to automatically determine what U-Net configuration(s) to use for test set prediction:

nnUNet_find_best_configuration -m 2d 3d_fullres 3d_lowres 3d_cascade_fullres -t XXX --strict

(all 5 folds need to be completed for all specified configurations!)

On datasets for which the cascade was not configured, use -m 2d 3d_fullres instead. If you wish to only explore some subset of the configurations, you can specify that with the -m command. We recommend setting the --strict (crash if one of the requested configurations is missing) flag. Additional options are available (use -h for help).

Run inference

Remember that the data located in the input folder must adhere to the format specified here.

nnUNet_find_best_configuration will print a string to the terminal with the inference commands you need to use. The easiest way to run inference is to simply use these commands.

If you wish to manually specify the configuration(s) used for inference, use the following commands:

For each of the desired configurations, run:


Only specify --save_npz if you intend to use ensembling. --save_npz will make the command save the softmax probabilities alongside of the predicted segmentation masks requiring a lot of disk space.

Please select a separate OUTPUT_FOLDER for each configuration!

If you wish to run ensembling, you can ensemble the predictions from several configurations with the following command:


You can specify an arbitrary number of folders, but remember that each folder needs to contain npz files that were generated by nnUNet_predict. For ensembling you can also specify a file that tells the command how to postprocess. These files are created when running nnUNet_find_best_configuration and are located in the respective trained model directory (RESULTS_FOLDER/nnUNet/CONFIGURATION/TaskXXX_MYTASK/TRAINER_CLASS_NAME__PLANS_FILE_IDENTIFIER/postprocessing.json or RESULTS_FOLDER/nnUNet/ensembles/TaskXXX_MYTASK/ensemble_X__Y__Z--X__Y__Z/postprocessing.json). You can also choose to not provide a file (simply omit -pp) and nnU-Net will not run postprocessing.

Note that per default, inference will be done with all available folds. We very strongly recommend you use all 5 folds. Thus, all 5 folds must have been trained prior to running inference. The list of available folds nnU-Net found will be printed at the start of the inference.

How to run inference with pretrained models

Trained models for all challenges we participated in are publicly available. They can be downloaded and installed directly with nnU-Net. Note that downloading a pretrained model will overwrite other models that were trained with exactly the same configuration (2d, 3d_fullres, ...), trainer (nnUNetTrainerV2) and plans.

To obtain a list of available models, as well as a short description, run


You can then download models by specifying their task name. For the Liver and Liver Tumor Segmentation Challenge, for example, this would be:

nnUNet_download_pretrained_model Task029_LiTS

After downloading is complete, you can use this model to run inference. Keep in mind that each of these models has specific data requirements (Task029_LiTS runs on abdominal CT scans, others require several image modalities as input in a specific order).

When using the pretrained models you must adhere to the license of the dataset they are trained on! If you run nnUNet_download_pretrained_model you will find a link where you can find the license for each dataset.


To get you started we compiled two simple to follow examples:

  • run a training with the 3d full resolution U-Net on the Hippocampus dataset. See here.
  • run inference with nnU-Net's pretrained models on the Prostate dataset. See here.

Usability not good enough? Let us know!

Extending or Changing nnU-Net

Please refer to this guide.

Information on run time and potential performance bottlenecks.

We have compiled a list of expected epoch times on standardized datasets across many different GPUs. You can use them to verify that your system is performing as expected. There are also tips on how to identify bottlenecks and what to do about them.

Click here.

Common questions and issues

We have collected solutions to common questions and problems. Please consult these documents before you open a new issue.

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