diff --git a/models.py b/models.py
index c924aa0b..e174b2bb 100755
--- a/models.py
+++ b/models.py
@@ -101,6 +101,9 @@ class YOLOLayer(nn.Module):
         self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
         self.weights = class_weights()
 
+        self.batch_count = 0
+        self.loss_means = torch.zeros(6)
+
     def forward(self, p, targets=None, requestPrecision=False):
         FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
 
@@ -139,6 +142,7 @@ class YOLOLayer(nn.Module):
 
         # Training
         if targets is not None:
+            self.batch_count += 1
             MSELoss = nn.MSELoss()
             BCEWithLogitsLoss = nn.BCEWithLogitsLoss()
             CrossEntropyLoss = nn.CrossEntropyLoss()
@@ -181,7 +185,15 @@ class YOLOLayer(nn.Module):
             # lconf += k * BCEWithLogitsLoss(pred_conf[~mask], mask[~mask].float())
 
             # Sum loss components
-            loss = lx + ly + lw + lh + lconf + lcls
+            balance_losses_flag = True
+            if balance_losses_flag:
+                loss_vec = torch.FloatTensor([lx.data, ly.data, lw.data, lh.data, lconf.data, lcls.data])
+                self.loss_means = self.loss_means * 0.99 + loss_vec * 0.01
+                k = 1 / self.loss_means.clone()
+                k /= k.sum()
+                loss = (lx * k[0] + ly * k[1] + lw * k[2] + lh * k[3] + lconf * k[4] + lcls * k[5]) * loss_vec.sum()
+            else:
+                loss = lx + ly + lw + lh + lconf + lcls
 
             # Sum False Positives from unassigned anchors
             i = torch.sigmoid(pred_conf[~mask]) > 0.5