diff --git a/train.py b/train.py
index f7458e5b..3d4f355e 100644
--- a/train.py
+++ b/train.py
@@ -240,17 +240,16 @@ def train(hyp):
             targets = targets.to(device)
 
             # Burn-in
-            if ni <= n_burn * 2:
-                model.gr = np.interp(ni, [0, n_burn * 2], [0.0, 1.0])  # giou loss ratio (obj_loss = 1.0 or giou)
-                if ni == n_burn:  # burnin complete
-                    print_model_biases(model)
-
+            if ni <= n_burn:
+                xi = [0, n_burn]  # x interp
+                model.gr = np.interp(ni, xi, [0.0, 1.0])  # giou loss ratio (obj_loss = 1.0 or giou)
+                accumulate = max(1, np.interp(ni, xi, [1, 64 / batch_size]).round())
                 for j, x in enumerate(optimizer.param_groups):
                     # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
-                    x['lr'] = np.interp(ni, [0, n_burn], [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
+                    x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
+                    x['weight_decay'] = np.interp(ni, xi, [0.0, hyp['weight_decay'] if j == 1 else 0.0])
                     if 'momentum' in x:
-                        x['momentum'] = np.interp(ni, [0, n_burn], [0.9, hyp['momentum']])
-
+                        x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])
 
             # Multi-Scale
             if opt.multi_scale: