questions and issues regarding training with nnUNetPlannerResEncL on BraTS2023

[neuronflow]

I am trying to train nnUNet on BraTS 2023 data (1251 exams).
The idea is to train on all cases and then evaluate on a separate test set.

With the standard 3d_fullres config, everything works without trouble.
However, nnUNet advertises I should use the residual encoders, so I try:

nnUNetv2_train 1337 3d_fullres all -p nnUNetPlannerResEncL -num_gpus 1

Notably, the training reaches pseudo dice 1 for some channels and .999 for others (can this flavor of nnUNet overfit the BraTS training set really so well?)

After training, I tried to run inference on our test set. Therefore, I used:

nnUNetv2_predict -i /input/imagesTs -o /output/predictions -d 1337 -f all -c 3d_fullres -p nnUNetResEncUNetLPlans 

For this to work, I had to manually copy some .json files. Am I using the wrong command here?

The resulting segmentations are terrible: