DCO Remediation Commit for Daniël Nobbe <[email protected]>

I, Daniël Nobbe <[email protected]>, hereby add my Signed-off-by to this commit: afce136
I, Daniël Nobbe <[email protected]>, hereby add my Signed-off-by to this commit: ffb501d
I, Daniël Nobbe <[email protected]>, hereby add my Signed-off-by to this commit: 5bf0ed6
I, Daniël Nobbe <[email protected]>, hereby add my Signed-off-by to this commit: 9e3d557
I, Daniël Nobbe <[email protected]>, hereby add my Signed-off-by to this commit: 79484f7

Signed-off-by: Daniël Nobbe <[email protected]>

Author: DanielNobbe

auto3dseg/README.md

@@ -64,6 +64,7 @@ The datalist format is based on the datasets released by the (Medical Segmentati
 See the function `load_decathlon_datalist` in `monai/data/decathlon_datalist.py` for a description of the format.
 
 For the AutoRunner, we only need the `training` data, since it will automatically create cross-validation folds.
+You are free to add the cross-validation folds beforehand, these should align with the number of folds set in the configuration of the AutoRunner (by default 5, see [notebook](notebooks/auto_runner.ipynb)).
 Any other metadata, such as `modality`, `numTraining`, `name`, etc. will not be used by the AutoRunner, but we do recommend to keep track of names and versions of the dataset.
 In short, your `datalist.json` file should look like this:
 
@@ -81,7 +82,6 @@ In short, your `datalist.json` file should look like this:
 ```
 
 The AutoRunner will create a `work_dir` folder in the directory from which it is ran, with the resulting models and the copied datalist file _with_ cross-validation folds. This allows you to see which datalist file the models are trained on.
-You are free to add the cross-validation folds beforehand, these should align with the number of folds set in the configuration of the AutoRunner (by default 5, see [notebook](notebooks/auto_runner.ipynb)).
 
 ## Reference Python APIs for Auto3DSeg