diff --git a/utils/gcp.sh b/utils/gcp.sh
index a7f9859e..95efe9fa 100755
--- a/utils/gcp.sh
+++ b/utils/gcp.sh
@@ -11,7 +11,7 @@ sudo reboot now
 # Re-clone
 sudo rm -rf yolov3
 # git clone https://github.com/ultralytics/yolov3  # master
-git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3  # branch
+git clone -b test --depth 1 https://github.com/ultralytics/yolov3 yolov3_test  # branch
 cp -r weights yolov3
 cp -r cocoapi/PythonAPI/pycocotools yolov3
 cd yolov3
diff --git a/utils/utils.py b/utils/utils.py
index 213f392a..5b4e9c76 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -3,6 +3,7 @@ import random
 from collections import defaultdict
 
 import cv2
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -10,6 +11,8 @@ import torch.nn as nn
 
 from utils import torch_utils
 
+matplotlib.rc('font', **{'family': 'normal', 'size': 11})
+
 # Set printoptions
 torch.set_printoptions(linewidth=1320, precision=5, profile='long')
 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
@@ -286,8 +289,8 @@ def compute_loss(p, targets):  # predictions, targets
     # Add to dictionary
     d = defaultdict(float)
     losses = [loss.item(), lxy.item(), lwh.item(), lconf.item(), lcls.item()]
-    for name, x in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
-        d[name] = x
+    for k, v in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
+        d[k] = v
 
     return loss, d
 
@@ -325,7 +328,7 @@ def build_targets(model, targets):
 
         # Width and height
         twh.append(torch.log(gwh / layer.anchor_vec[a]))  # yolo method
-        # twh.append(torch.sqrt(gwh / layer.anchor_vec[a]) / 2)  # power method
+        # twh.append((gwh / layer.anchor_vec[a]) ** (1 / 3) / 2)  # power method
 
         # Class
         tcls.append(c)
@@ -468,7 +471,7 @@ def plot_wh_methods():  # from utils.utils import *; plot_wh_methods()
     # https://github.com/ultralytics/yolov3/issues/168
     x = np.arange(-4.0, 4.0, .1)
     ya = np.exp(x)
-    yb = (torch.sigmoid(torch.from_numpy(x)).numpy() * 2)
+    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
 
     fig = plt.figure(figsize=(6, 3), dpi=150)
     plt.plot(x, ya, '.-', label='yolo method')
@@ -495,7 +498,7 @@ def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
         x = range(start, stop if stop else results.shape[1])
         for i in range(10):
             plt.subplot(2, 5, i + 1)
-            plt.plot(x, results[i, x], marker='.', label=f)
+            plt.plot(x, results[i, x].clip(max=500), marker='.', label=f)
             plt.title(s[i])
             if i == 0:
                 plt.legend()