diff --git a/utils/loss.py b/utils/loss.py
index 02f945a767c3..14eed55c665f 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -162,7 +162,7 @@ def __call__(self, preds, targets, seg_masks):  # predictions, targets
 
                 # Mask Loss
                 # print('\n----------- PRED VALID: ', torch.all(pred_mask >= 0), '-----------------\n')
-                print('\n----------- SEG MASK VALID: ', torch.all(seg_masks >= 0), '-----------------\n')
+                # print('\n----------- SEG MASK VALID: ', torch.all(seg_masks >= 0), '-----------------\n')
                 seg_loss = nn.functional.binary_cross_entropy_with_logits(pred_mask, seg_masks, reduction='none').mean()
                 lseg += seg_loss
 
diff --git a/val.py b/val.py
index b73a3f79c276..fee9fadf6258 100644
--- a/val.py
+++ b/val.py
@@ -76,8 +76,8 @@ def compute_seg_iou(pred, target, n_classes=2):
     pred[pred >= 0.5] = 1
     pred = pred.view(-1)
     target = target.view(-1)
-    print(target)
-    print(pred)
+    # print(target)
+    # print(pred)
 
     # Ignore IoU for background class ("0")
     for cls in range(1, n_classes):  # This goes from 1:n_classes-1 -> class "0" is ignored