diff --git a/utils/callbacks.py b/utils/callbacks.py
index a703585e5c101318bb2d35206cccfab208dc6ae5..b866781442081876c9127500b38e4f279e3d6f68 100644
--- a/utils/callbacks.py
+++ b/utils/callbacks.py
@@ -148,9 +148,9 @@ class EvalCallback():
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_rboxes[:, [0, 2]]  *= image_shape[1]
-            top_rboxes[:, [1, 3]]  *= image_shape[0]
             top_polys   = rbox2poly(top_rboxes)
+            top_polys[:, 0::2] *= image_shape[1]
+            top_polys[:, 1::2] *= image_shape[0]
             top_hbbs    = poly2hbb(top_polys)
         top_100     = np.argsort(top_conf)[::-1][:self.max_boxes]
         top_hbbs    = top_hbbs[top_100]
diff --git a/yolo.py b/yolo.py
index 28bb9ddfaaa6462ad59a8c8c9cf19d4b25b366aa..d538f5827f410c1c17febb009aa1a130b576aa2a 100644
--- a/yolo.py
+++ b/yolo.py
@@ -157,9 +157,9 @@ class YOLO(object):
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_rboxes[:, [0, 2]]  *= image_shape[1]
-            top_rboxes[:, [1, 3]]  *= image_shape[0]
             top_polys   = rbox2poly(top_rboxes)
+            top_polys[:, 0::2] *= image_shape[1]
+            top_polys[:, 1::2] *= image_shape[0]
         #---------------------------------------------------------#
         #   设置字体与边框厚度
         #---------------------------------------------------------#
@@ -378,9 +378,9 @@ class YOLO(object):
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_rboxes[:, [0, 2]]  *= image_shape[1]
-            top_rboxes[:, [1, 3]]  *= image_shape[0]
             top_polys   = rbox2poly(top_rboxes)
+            top_polys[:, 0::2] *= image_shape[1]
+            top_polys[:, 1::2] *= image_shape[0]
             top_hbbs    = poly2hbb(top_polys)
         for i, c in list(enumerate(top_label)):
             predicted_class = self.class_names[int(c)]