diff --git a/train.py b/train.py
index 1473b939..19cd2c44 100644
--- a/train.py
+++ b/train.py
@@ -249,6 +249,10 @@ def train():
                 print('WARNING: nan loss detected, ending training')
                 return results
 
+            # Divide by accumulation count
+            if accumulate > 1:
+                loss /= accumulate
+
             # Compute gradient
             if mixed_precision:
                 with amp.scale_loss(loss, optimizer) as scaled_loss:
diff --git a/utils/utils.py b/utils/utils.py
index 2eaa61b1..ef175c10 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -326,8 +326,6 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     FCE = FocalLoss(nn.CrossEntropyLoss())  # weight=model.class_weights
 
     # Compute losses
-    bs = p[0].shape[0]  # batch size
-    k = bs / 64  # loss gain
     for i, pi in enumerate(p):  # layer index, layer predictions
         b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
         tobj = torch.zeros_like(pi[..., 0])  # target obj
@@ -370,9 +368,9 @@ def compute_loss(p, targets, model):  # predictions, targets, model
                 t[b, a, gj, gi, tcls[i]] = 1.0
             lobj += FBCE(pi[..., 5:], t)
 
-    lbox *= k * h['giou']
-    lobj *= k * h['obj']
-    lcls *= k * h['cls']
+    lbox *= h['giou']
+    lobj *= h['obj']
+    lcls *= h['cls']
     loss = lbox + lobj + lcls
     return loss, torch.cat((lbox, lobj, lcls, loss)).detach()