diff --git a/utils/utils.py b/utils/utils.py
index cb105ed6..be47c775 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -748,13 +748,12 @@ def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_re
     plt.savefig('evolve.png', dpi=200)
 
 
-def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results2()
+def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
     # Plot training results files 'results*.txt'
-
     fig, ax = plt.subplots(2, 5, figsize=(14, 7))
     ax = ax.ravel()
     s = ['GIoU', 'Confidence', 'Classification', 'Precision', 'Recall',
-         'GIoU val', 'val Confidence', 'val Classification', 'mAP', 'F1']
+         'val GIoU', 'val Confidence', 'val Classification', 'mAP', 'F1']
     for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
         results = np.loadtxt(f, usecols=[2, 4, 5, 9, 10, 13, 14, 15, 11, 12]).T
         n = results.shape[1]  # number of rows
@@ -767,14 +766,32 @@ def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results2()
     fig.savefig('results.png', dpi=200)
 
 
+def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_results_overlay()
+    # Plot training results files 'results*.txt', overlaying train and val losses
+    fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
+    ax = ax.ravel()
+    s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1']  # legends
+    t = ['GIoU', 'Confidence', 'Classification', 'P-R', 'mAP-F1']  # titles
+    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
+        results = np.loadtxt(f, usecols=[2, 4, 5, 9, 11, 13, 14, 15, 10,  12]).T
+        n = results.shape[1]  # number of rows
+        x = range(start, min(stop, n) if stop else n)
+        for i in range(5):
+            ax[i].plot(x, results[i, x], marker='.', label=s[i])
+            ax[i].plot(x, results[i+5, x], marker='.', label=s[i+5])
+            ax[i].set_title(t[i])
+            ax[i].legend()
+    fig.tight_layout()
+    fig.savefig('results.png', dpi=200)
+
+
 def plot_results_orig(start=0, stop=0):  # from utils.utils import *; plot_results_orig()
     # Plot training results files 'results*.txt' in original format
-
     fig, ax = plt.subplots(2, 5, figsize=(14, 7))
     ax = ax.ravel()
     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')):
+    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Google Drive/results*.txt')):
         results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11, 12, 13]).T
         n = results.shape[1]  # number of rows
         x = range(start, min(stop, n) if stop else n)