diff --git a/utils/utils.py b/utils/utils.py
index 38ad3242..0b28d7c9 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -321,8 +321,8 @@ def compute_loss(p, targets, model, arc='default'):  # predictions, targets, mod
     # Define criteria
     BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
     BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
-    BCE = nn.BCEWithLogitsLoss()
-    CE = nn.CrossEntropyLoss()  # weight=model.class_weights
+    FBCE = FocalLoss(nn.BCEWithLogitsLoss())
+    FCE = FocalLoss(nn.CrossEntropyLoss())  # weight=model.class_weights
 
     # Compute losses
     bs = p[0].shape[0]  # batch size
@@ -361,13 +361,13 @@ def compute_loss(p, targets, model, arc='default'):  # predictions, targets, mod
             t = torch.zeros_like(pi[..., 0], dtype=torch.long)  # targets
             if nb:
                 t[b, a, gj, gi] = tcls[i] + 1
-            lcls += CE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
+            lcls += FCE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
 
         elif arc == 'uBCE':  # unified BCE (1 background + 80 classes), hyps 200-30
             t = torch.zeros_like(pi[..., 5:])  # targets
             if nb:
                 t[b, a, gj, gi, tcls[i]] = 1.0
-            lobj += BCE(pi[..., 5:], t)
+            lobj += FBCE(pi[..., 5:], t)
 
     lbox *= k * h['giou']
     lobj *= k * h['obj']