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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.
nnU-Net expects datasets in a structured format. This format is inspired by the data structure of the Medical Segmentation Decthlon. Please read this for information on how to set up datasets to be compatible with nnU-Net.
Since version 2 we support multiple image file formats (.nii.gz, .png, .tif, ...)! Read the dataset_format documentation to learn more!
Datasets from nnU-Net v1 can be converted to V2 by running nnUNetv2_convert_old_nnUNet_dataset INPUT_FOLDER OUTPUT_DATASET_NAME. Remember that v2 calls datasets DatasetXXX_Name (not Task) where XXX is a 3-digit number.
Please provide the path to the old task, not just the Task name. nnU-Net V2 doesn't know where v1 tasks were!
Given a new dataset, nnU-Net will extract a dataset fingerprint (a set of dataset-specific properties such as image sizes, voxel spacings, intensity information etc). This information is used to design three U-Net configurations. Each of these pipelines operates on its own preprocessed version of the dataset.
The easiest way to run fingerprint extraction, experiment planning and preprocessing is to use:
nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrityWhere DATASET_ID is the dataset id (duh). We recommend --verify_dataset_integrity whenever it's the first time
you run this command. This will check for some of the most common error sources!
You can also process several datasets at once by giving -d 1 2 3 [...]. If you already know what U-Net configuration
you need you can also specify that with -c 3d_fullres (make sure to adapt -np in this case!). For more information
about all the options available to you please run nnUNetv2_plan_and_preprocess -h.
nnUNetv2_plan_and_preprocess will create a new subfolder in your nnUNet_preprocessed folder named after the dataset. Once the command is completed there will be a dataset_fingerprint.json file as well as a nnUNetPlans.json file for you to look at (in case you are interested!). There will also be subfolders containing the preprocessed data for your UNet configurations.
[Optional]
If you prefer to keep things separate, you can also use nnUNetv2_extract_fingerprint, nnUNetv2_plan_experiment
and nnUNetv2_preprocess (in that order).
You pick which configurations (2d, 3d_fullres, 3d_lowres, 3d_cascade_fullres) should be trained! If you have no idea what performs best on your data, just run all of them and let nnU-Net identify the best one. It's up to you!
nnU-Net trains all configurations in a 5-fold cross-validation over the training cases. This is 1) needed so that nnU-Net can estimate the performance of each configuration and tell you which one should be used for your segmentation problem and 2) a natural way of obtaining a good model ensemble (average the output of these 5 models for prediction) to boost performance.
You can influence the splits nnU-Net uses for 5-fold cross-validation (see here). If you prefer to train a single model on all training cases, this is also possible (see below).
Note that not all U-Net configurations are created for all datasets. In datasets with small image sizes, the U-Net cascade (and with it the 3d_lowres configuration) 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 nnUNetv2_train command. The general structure of the command is:
nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD [additional options, see -h]UNET_CONFIGURATION is a string that identifies the requested U-Net configuration (defaults: 2d, 3d_fullres, 3d_lowres, 3d_cascade_lowres). DATASET_NAME_OR_ID specifies what dataset should be trained on and FOLD specifies which fold of the 5-fold-cross-validation 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: If you plan to use nnUNetv2_find_best_configuration (see below) add the --npz flag. This makes
nnU-Net save the softmax outputs during the final validation. They are needed for that. Exported softmax
predictions are very large and therefore can take up a lot of disk space, which is why this is not enabled by default.
If you ran initially without the --npz flag but now require the softmax predictions, simply rerun the validation with:
nnUNetv2_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD --val --npzYou can specify the device nnU-net should use by using -device DEVICE. DEVICE can only be cpu, cuda or mps. If
you have multiple GPUs, please select the gpu id using CUDA_VISIBLE_DEVICES=X nnUNetv2_train [...] (requires device to be cuda).
See nnUNetv2_train -h for additional options.
For FOLD in [0, 1, 2, 3, 4], run:
nnUNetv2_train DATASET_NAME_OR_ID 2d FOLD [--npz]For FOLD in [0, 1, 2, 3, 4], run:
nnUNetv2_train DATASET_NAME_OR_ID 3d_fullres FOLD [--npz]For FOLD in [0, 1, 2, 3, 4], run:
nnUNetv2_train DATASET_NAME_OR_ID 3d_lowres FOLD [--npz]For FOLD in [0, 1, 2, 3, 4], run:
nnUNetv2_train DATASET_NAME_OR_ID 3d_cascade_fullres 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!
The trained models will be written to the nnUNet_results folder. Each training obtains an automatically generated output folder name:
nnUNet_results/DatasetXXX_MYNAME/TRAINER_CLASS_NAME__PLANS_NAME__CONFIGURATION/FOLD
For Dataset002_Heart (from the MSD), for example, this looks like this:
nnUNet_results/
├── Dataset002_Heart
│── nnUNetTrainer__nnUNetPlans__2d
│ ├── fold_0
│ ├── fold_1
│ ├── fold_2
│ ├── fold_3
│ ├── fold_4
│ ├── dataset.json
│ ├── dataset_fingerprint.json
│ └── plans.json
└── nnUNetTrainer__nnUNetPlans__3d_fullres
├── fold_0
├── fold_1
├── fold_2
├── fold_3
├── fold_4
├── dataset.json
├── dataset_fingerprint.json
└── plans.json
Note that 3d_lowres and 3d_cascade_fullres do not exist here because this dataset did not trigger the cascade. In each model training output folder (each of the fold_x folder), the following files will be created:
- debug.json: Contains a summary of blueprint and inferred parameters used for training this model as well as a bunch of additional stuff. Not easy to read, but very useful for debugging ;-)
- checkpoint_best.pth: checkpoint files of the best model identified during training. Not used right now unless you explicitly tell nnU-Net to use it.
- checkpoint_final.pth: checkpoint file 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: Shows losses, pseudo dice, learning rate and epoch times ofer the course of the training. At the top is a plot of the training (blue) and validation (red) loss during training. Also shows an approximation of the dice (green) as well as a moving average of it (dotted green line). This approximation is the average Dice score of the foreground classes. It needs to be taken with a big (!) 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 takes way too long to be done each epoch. It 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 file in here
contains the validation metrics (a mean over all cases is provided at the start of the file). If
--npzwas set then the compressed softmax outputs (saved as .npz files) are in here as well.
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 that all network trainings take less than 2 days. Refer to our benchmarks to see if your system is performing as expected.
If multiple GPUs are at your disposal, the best way of using them is to train multiple nnU-Net trainings at once, one on each GPU. This is because data parallelism never scales perfectly linearly, especially not with small networks such as the ones used by nnU-Net.
Example:
CUDA_VISIBLE_DEVICES=0 nnUNetv2_train DATASET_NAME_OR_ID 2d 0 [--npz] & # train on GPU 0
CUDA_VISIBLE_DEVICES=1 nnUNetv2_train DATASET_NAME_OR_ID 2d 1 [--npz] & # train on GPU 1
CUDA_VISIBLE_DEVICES=2 nnUNetv2_train DATASET_NAME_OR_ID 2d 2 [--npz] & # train on GPU 2
CUDA_VISIBLE_DEVICES=3 nnUNetv2_train DATASET_NAME_OR_ID 2d 3 [--npz] & # train on GPU 3
CUDA_VISIBLE_DEVICES=4 nnUNetv2_train DATASET_NAME_OR_ID 2d 4 [--npz] & # train on GPU 4
...
waitImportant: The first time a training is run nnU-Net will extract the preprocessed data into uncompressed numpy arrays for speed reasons! This operation must be completed before starting more than one training of the same configuration! Wait with starting subsequent folds until the first training is using the GPU! Depending on the dataset size and your System this should only take a couple of minutes at most.
If you insist on running DDP multi-GPU training, we got you covered:
nnUNetv2_train DATASET_NAME_OR_ID 2d 0 [--npz] -num_gpus X
Again, note that this will be slower than running separate training on separate GPUs. DDP only makes sense if you have manually interfered with the nnU-Net configuration and are training larger models with larger patch and/or batch sizes!
Important when using -num_gpus:
- If you train using, say, 2 GPUs but have more GPUs in the system you need to specify which GPUs should be used via CUDA_VISIBLE_DEVICES=0,1 (or whatever your ids are).
- You cannot specify more GPUs than you have samples in your minibatches. If the batch size is 2, 2 GPUs is the maximum!
- Make sure your batch size is divisible by the numbers of GPUs you use or you will not make good use of your hardware.
In contrast to the old nnU-Net, DDP is now completely hassle free. Enjoy!
Once the desired configurations were trained (full cross-validation) you can tell nnU-Net to automatically identify the best combination for you:
nnUNetv2_find_best_configuration DATASET_NAME_OR_ID -c CONFIGURATIONS
CONFIGURATIONS hereby is the list of configurations you would like to explore. Per default, ensembling is enabled
meaning that nnU-Net will generate all possible combinations of ensembles (2 configurations per ensemble). This requires
the .npz files containing the predicted probabilities of the validation set to be present (use nnUNetv2_train with
--npz flag, see above). You can disable ensembling by setting the --disable_ensembling flag.
See nnUNetv2_find_best_configuration -h for more options.
nnUNetv2_find_best_configuration will also automatically determine the postprocessing that should be used. Postprocessing in nnU-Net only considers the removal of all but the largest component in the prediction (once for foreground vs background and once for each label/region).
Once completed, the command will print to your console exactly what commands you need to run to make predictions. It
will also create two files in the nnUNet_results/DATASET_NAME folder for you to inspect:
inference_instructions.txtagain contains the exact commands you need to use for predictionsinference_information.jsoncan be inspected to see the performance of all configurations and ensembles, as well as the effect of the postprocessing plus some debug information.
Remember that the data located in the input folder must have the file endings as the dataset you trained the model on and must adhere to the nnU-Net naming scheme for image files (see dataset format and inference data format!)
nnUNetv2_find_best_configuration (see above) 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:
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_NAME_OR_ID -c CONFIGURATION --save_probabilities
Only specify --save_probabilities if you intend to use ensembling. --save_probabilities will make the command save the predicted
probabilities alongside of the predicted segmentation masks requiring a lot of disk space.
Please select a separate OUTPUT_FOLDER for each configuration!
Note that per default, inference will be done with all 5 folds from the cross-validation as an ensemble. We very strongly recommend you use all 5 folds. Thus, all 5 folds must have been trained prior to running inference.
If you wish to make predictions with a single model, train the all fold and specify it in nnUNetv2_predict
with -f all
If you wish to ensemble multiple predictions (typically form different configurations), you can do so with the following command:
nnUNetv2_ensemble -i FOLDER1 FOLDER2 ... -o OUTPUT_FOLDER -np NUM_PROCESSESYou can specify an arbitrary number of folders, but remember that each folder needs to contain npz files that were
generated by nnUNetv2_predict. Again, nnUNetv2_ensemble -h will tell you more about additional options.
Finally, apply the previously determined postprocessing to the (ensembled) predictions:
nnUNetv2_apply_postprocessing -i FOLDER_WITH_PREDICTIONS -o OUTPUT_FOLDER --pp_pkl_file POSTPROCESSING_FILE -plans_json PLANS_FILE -dataset_json DATASET_JSON_FILE
nnUNetv2_find_best_configuration (or its generated inference_instructions.txt file) will tell you where to find
the postprocessing file. If not you can just look for it in your results folder (it's creatively named
postprocessing.pkl). If your source folder is from an ensemble, you also need to specify a -plans_json file and
a -dataset_json file that should be used (for single configuration predictions these are automatically copied
from the respective training). You can pick these files from any of the ensemble members.
See here