diff --git a/train.py b/train.py
index 5b138f61..0ab05932 100644
--- a/train.py
+++ b/train.py
@@ -167,6 +167,10 @@ def train(
         from apex import amp
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
 
+    # Remove old results
+    for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg') + 'results.txt':
+        os.remove(f)
+
     # Start training
     model.hyp = hyp  # attach hyperparameters to model
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
@@ -175,8 +179,6 @@ def train(
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0)  # P, R, mAP, F1, test_loss
     n_burnin = min(round(nb / 5 + 1), 1000)  # burn-in batches
-    for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg'):
-        os.remove(f)
     t, t0 = time.time(), time.time()
     for epoch in range(start_epoch, epochs):
         model.train()
@@ -185,7 +187,7 @@ def train(
         # Update scheduler
         scheduler.step()
 
-        # Freeze backbone at epoch 0, unfreeze at epoch 1
+        # Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
         if freeze_backbone and epoch < 2:
             for name, p in model.named_parameters():
                 if int(name.split('.')[1]) < cutoff:  # if layer < 75
@@ -200,7 +202,6 @@ def train(
         for i, (imgs, targets, _, _) in enumerate(dataloader):
             imgs = imgs.to(device)
             targets = targets.to(device)
-            nt = len(targets)
 
             # Plot images with bounding boxes
             if epoch == 0 and i == 0:
@@ -233,13 +234,11 @@ def train(
                 optimizer.step()
                 optimizer.zero_grad()
 
-            # Update running mean of tracked metrics
-            mloss = (mloss * i + loss_items) / (i + 1)
-
             # Print batch results
+            mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
             s = ('%8s%12s' + '%10.3g' * 7) % (
                 '%g/%g' % (epoch, epochs - 1),
-                '%g/%g' % (i, nb - 1), *mloss, nt, time.time() - t)
+                '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
             t = time.time()
             print(s)