diff --git a/train.py b/train.py
index 6e4c78b8..a9a98c4b 100644
--- a/train.py
+++ b/train.py
@@ -171,7 +171,7 @@ def train(cfg,
     for epoch in range(start_epoch, epochs):
         model.train()
         print(('\n%8s' + '%10s' * 8) %
-              ('Epoch', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size', 'gpu_mem'))
+              ('Epoch', 'gpu_mem', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size'))
 
         # Update scheduler
         scheduler.step()
@@ -236,7 +236,7 @@ def train(cfg,
             # Print batch results
             mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
             mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0  # (GB)
-            s = ('%8s' + '%10.3g' * 8) % ('%g/%g' % (epoch, epochs - 1), *mloss, len(targets), img_size, mem)
+            s = ('%8s' + '%10.3g' * 8) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
             pbar.set_description(s)  # print(s)
 
         # Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
diff --git a/utils/utils.py b/utils/utils.py
index 6f492fcf..d8d4809a 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -685,7 +685,7 @@ def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
 
     fig, ax = plt.subplots(2, 5, figsize=(14, 7))
     ax = ax.ravel()
-    s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Train Loss', 'Precision', 'Recall', 'mAP', 'F1',
+    s = ['GIoU/XY', 'Width/Height', 'Confidence', 'Classification', 'Train Loss', 'Precision', 'Recall', 'mAP', 'F1',
          'Test Loss']
     for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
         results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11, 12, 13]).T