| # Example: 3D U-Net training on the Hippocampus dataset | |
| This is a step-by-step example on how to run a 3D full resolution Training with the Hippocampus dataset from the | |
| Medical Segmentation Decathlon. | |
| 1) Install nnU-Net by following the instructions [here](../readme.md#installation). Make sure to set all relevant paths, | |
| also see [here](setting_up_paths.md). This step is necessary so that nnU-Net knows where to store raw data, | |
| preprocessed data and trained models. | |
| 2) Download the Hippocampus dataset of the Medical Segmentation Decathlon from | |
| [here](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2). Then extract the archive to a | |
| destination of your choice. | |
| 3) Decathlon data come as 4D niftis. This is not compatible with nnU-Net (see dataset format specified | |
| [here](dataset_conversion.md)). Convert the Hippocampus dataset into the correct format with | |
| ```bash | |
| nnUNet_convert_decathlon_task -i /xxx/Task04_Hippocampus | |
| ``` | |
| Note that `Task04_Hippocampus` must be the folder that has the three 'imagesTr', 'labelsTr', 'imagesTs' subfolders! | |
| The converted dataset can be found in $nnUNet_raw_data_base/nnUNet_raw_data ($nnUNet_raw_data_base is the folder for | |
| raw data that you specified during installation) | |
| 4) You can now run nnU-Nets pipeline configuration (and the preprocessing) with the following line: | |
| ```bash | |
| nnUNet_plan_and_preprocess -t 4 | |
| ``` | |
| Where 4 refers to the task ID of the Hippocampus dataset. | |
| 5) Now you can already start network training. This is how you train a 3d full resoltion U-Net on the Hippocampus dataset: | |
| ```bash | |
| nnUNet_train 3d_fullres nnUNetTrainerV2 4 0 | |
| ``` | |
| nnU-Net per default requires all trainings as 5-fold cross validation. The command above will run only the training for the | |
| first fold (fold 0). 4 is the task identifier of the hippocampus dataset. Training one fold should take about 9 | |
| hours on a modern GPU. | |
| This tutorial is only intended to demonstrate how easy it is to get nnU-Net running. You do not need to finish the | |
| network training - pretrained models for the hippocampus task are available (see [here](../readme.md#run-inference)). | |
| The only prerequisite for running nnU-Net on your custom dataset is to bring it into a structured, nnU-Net compatible | |
| format. nnU-Net will take care of the rest. See [here](dataset_conversion.md) for instructions on how to convert | |
| datasets into nnU-Net compatible format. | |