diff --git a/configs/dataset/biomassters.yaml b/configs/dataset/biomassters.yaml
index c4e5561..d98014a 100644
--- a/configs/dataset/biomassters.yaml
+++ b/configs/dataset/biomassters.yaml
@@ -4,7 +4,7 @@ root_path: ./data/Biomassters
 download_url: 
 auto_download: False
 img_size: 256
-temp: 6 #6 (select month to use if single temporal (multi_temp : 1))
+temp: 6 #6 (select month to use if single temporal (multi_temporal : 1))
 multi_temporal: 12
 multi_modal: True
 
@@ -35,12 +35,12 @@ bands:
     - B12
     - CLP
   sar:
-    - ASC_VV
-    - ASC_VH
+    - VV #set band name to match the input band name of the model e.g. VV for CROMA, ASC_VV for DOFA
+    - VH #set band name to match the input band name of the model e.g. VH for CROMA, ASC_VH for DOFA
     - DSC_VV
     - DSC_VH
 
-# TODO: fix the normalization 
+# TODO: add mean and std normalization values 
 data_mean:
   optical: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   sar: [0, 0, 0, 0]
diff --git a/pangaea/engine/evaluator.py b/pangaea/engine/evaluator.py
index 0455c4b..d3c770c 100644
--- a/pangaea/engine/evaluator.py
+++ b/pangaea/engine/evaluator.py
@@ -372,16 +372,16 @@ def evaluate(self, model, model_name='model', model_ckpt_path=None):
             if self.inference_mode == "sliding":
                 input_size = model.module.encoder.input_size
                 logits = self.sliding_inference(model, image, input_size, output_shape=target.shape[-2:],
-                                                max_batch=self.sliding_inference_batch)
+                                                max_batch=self.sliding_inference_batch).squeeze(dim=1)
             elif self.inference_mode == "whole":
                 logits = model(image, output_shape=target.shape[-2:]).squeeze(dim=1)
             else:
                 raise NotImplementedError((f"Inference mode {self.inference_mode} is not implemented."))
 
-            mse += F.mse_loss(logits, target, reduction='sum')
+            mse += F.mse_loss(logits, target)
 
         torch.distributed.all_reduce(mse, op=torch.distributed.ReduceOp.SUM)
-        mse = mse / len(self.val_loader.dataset)
+        mse = mse / len(self.val_loader)
 
         metrics = {"MSE": mse.item(), "RMSE": torch.sqrt(mse).item()}
         self.log_metrics(metrics)