diff --git a/train.py b/train.py
index 57aabca4..147eb237 100644
--- a/train.py
+++ b/train.py
@@ -32,9 +32,8 @@ def train(
     else:
         torch.backends.cudnn.benchmark = True
 
-    os.makedirs(weights, exist_ok=True)
-    latest_weights_file = os.path.join(weights, 'latest.pt')
-    best_weights_file = os.path.join(weights, 'best.pt')
+    latest = os.path.join(weights, 'latest.pt')
+    best = os.path.join(weights, 'best.pt')
 
     # Configure run
     data_cfg = parse_data_cfg(data_cfg)
@@ -49,7 +48,7 @@ def train(
 
     lr0 = 0.001
     if resume:
-        checkpoint = torch.load(latest_weights_file, map_location='cpu')
+        checkpoint = torch.load(latest, map_location='cpu')
 
         model.load_state_dict(checkpoint['model'])
         if torch.cuda.device_count() > 1:
@@ -185,18 +184,18 @@ def train(
                       'best_loss': best_loss,
                       'model': model.state_dict(),
                       'optimizer': optimizer.state_dict()}
-        torch.save(checkpoint, latest_weights_file)
+        torch.save(checkpoint, latest)
 
         # Save best checkpoint
         if best_loss == loss_per_target:
-            os.system('cp ' + latest_weights_file + ' ' + best_weights_file)
+            os.system('cp ' + latest + ' ' + best)
 
         # Save backup weights every 5 epochs
         if (epoch > 0) & (epoch % 5 == 0):
-            os.system('cp ' + latest_weights_file + ' ' + os.path.join(weights, 'backup{}.pt'.format(epoch)))
+            os.system('cp ' + latest + ' ' + os.path.join(weights, 'backup{}.pt'.format(epoch)))
 
         # Calculate mAP
-        mAP, R, P = test.test(cfg, data_cfg, weights=latest_weights_file, batch_size=batch_size, img_size=img_size)
+        mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
 
         # Write epoch results
         with open('results.txt', 'a') as file: