diff --git a/src/super_gradients/recipes/cityscapes_kd_base.yaml b/src/super_gradients/recipes/cityscapes_kd_base.yaml
index 40d0d6e2b4..b6592f5948 100644
--- a/src/super_gradients/recipes/cityscapes_kd_base.yaml
+++ b/src/super_gradients/recipes/cityscapes_kd_base.yaml
@@ -1,7 +1,9 @@
 # Distillation for semantic segmentation on Cityscapes dataset.
 #
 # Instructions:
-#   0. Make sure that the data is stored in dataset_params.dataset_dir or add "dataset_params.data_dir=<PATH-TO-DATASET>" at the end of the command below (feel free to check ReadMe)
+#   0. Make sure that the data is stored in dataset_params.[train/val]_dataset_params.root_dir or
+#      add "dataset_params.[train/val]_dataset_params.root_dir=<PATH-TO-DATASET>" at the end of the
+#      command below (feel free to check ReadMe)
 #   1. Move to the project root (where you will find the ReadMe and src folder)
 #   2. Run the command:
 #      DDRNet23:        python -m super_gradients.train_from_kd_recipe --config-name=cityscapes_kd_base student_architecture=ddrnet_23
@@ -55,7 +57,7 @@ training_hyperparams:
     weights: [ 1. ]
     kd_loss_weights: [1., 6.]
 
-    KDLogitsLoss:
+    kd_loss:
       _target_: super_gradients.training.losses.cwd_loss.ChannelWiseKnowledgeDistillationLoss
       temperature: 3.
       normalization_mode: channel_wise