add info to experiment planning, add reference to updated configurati…

…ons to how_to_use_nnunet.md

documentation/how_to_use_nnunet.md

@@ -1,4 +1,10 @@
+## **2024-04-18 UPDATE: New residual encoder UNet presets available!**
+The recommended nnU-Net presets have changed! See [here](resenc_presets.md) how to unlock them!
+
+
 ## 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.

nnunetv2/experiment_planning/plan_and_preprocess_api.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import List, Type, Optional, Tuple, Union
 
 from batchgenerators.utilities.file_and_folder_operations import join, maybe_mkdir_p, load_json
@@ -79,6 +80,12 @@ def plan_experiments(dataset_ids: List[int], experiment_planner_class_name: str
     """
     overwrite_target_spacing ONLY applies to 3d_fullres and 3d_cascade fullres!
     """
+    if experiment_planner_class_name == 'ExperimentPlanner':
+        print("\n############################\n"
+              "INFO: You are using the old nnU-Net default planner. We have updated our recommendations. "
+              "Please consider using those instead! "
+              "Read more here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md"
+              "\n############################\n")
     experiment_planner = recursive_find_python_class(join(nnunetv2.__path__[0], "experiment_planning"),
                                                      experiment_planner_class_name,
                                                      current_module="nnunetv2.experiment_planning")

readme.md

@@ -4,7 +4,7 @@ Click [here](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1) if you were looki
 
 Coming from V1? Check out the [TLDR Migration Guide](documentation/tldr_migration_guide_from_v1.md). Reading the rest of the documentation is still strongly recommended ;-)
 
-## **2024-04-12 UPDATE: New residual encoder UNet presets available!**
+## **2024-04-18 UPDATE: New residual encoder UNet presets available!**
 Residual encoder UNet presets substantially improve segmentation performance.
 They ship for a variety of GPU memory targets. It's all awesome stuff, promised! 
 Read more :point_right: [here](documentation/resenc_presets.md) :point_left: