diff --git a/detect.py b/detect.py
index f9b51a2f..f5156025 100644
--- a/detect.py
+++ b/detect.py
@@ -14,7 +14,7 @@ def detect(
         images,
         output='output',  # output folder
         img_size=416,
-        conf_thres=0.3,
+        conf_thres=0.5,
         nms_thres=0.5,
         save_txt=False,
         save_images=True,
diff --git a/utils/utils.py b/utils/utils.py
index 7509f555..313cb1e2 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -358,7 +358,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
 
         # Filter out confidence scores below threshold
         class_conf, class_pred = pred[:, 5:].max(1)
-        # pred[:, 4] *= class_conf
+        pred[:, 4] *= class_conf
 
         i = (pred[:, 4] > conf_thres) & (pred[:, 2] > min_wh) & (pred[:, 3] > min_wh)
         pred = pred[i]
@@ -373,7 +373,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
 
         # Box (center x, center y, width, height) to (x1, y1, x2, y2)
         pred[:, :4] = xywh2xyxy(pred[:, :4])
-        pred[:, 4] *= class_conf  # improves mAP from 0.549 to 0.551
+        # pred[:, 4] *= class_conf  # improves mAP from 0.549 to 0.551
 
         # Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
         pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
@@ -479,7 +479,7 @@ def plot_wh_methods():  # from utils.utils import *; plot_wh_methods()
     fig.savefig('comparison.jpg', dpi=fig.dpi)
 
 
-def plot_results(start=0):  # from utils.utils import *; plot_results()
+def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
     # Plot training results files 'results*.txt'
     # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
 
@@ -487,7 +487,7 @@ def plot_results(start=0):  # from utils.utils import *; plot_results()
     s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
     for f in sorted(glob.glob('results*.txt')):
         results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T  # column 11 is mAP
-        x = range(start, results.shape[1])
+        x = range(start, stop if stop else results.shape[1])
         for i in range(8):
             plt.subplot(2, 4, i + 1)
             plt.plot(x, results[i, x], marker='.', label=f)