diff --git a/models.py b/models.py
index 905797d2..01dc1edd 100755
--- a/models.py
+++ b/models.py
@@ -21,20 +21,20 @@ def create_modules(module_defs, img_size):
         if mdef['type'] == 'convolutional':
             bn = mdef['batch_normalize']
             filters = mdef['filters']
-            size = mdef['size']
+            k = mdef['size']  # kernel size
             stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
-            if isinstance(size, int):  # single-size conv
+            if isinstance(k, int):  # single-size conv
                 modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],
                                                        out_channels=filters,
-                                                       kernel_size=size,
+                                                       kernel_size=k,
                                                        stride=stride,
-                                                       padding=size // 2 if mdef['pad'] else 0,
+                                                       padding=k // 2 if mdef['pad'] else 0,
                                                        groups=mdef['groups'] if 'groups' in mdef else 1,
                                                        bias=not bn))
             else:  # multiple-size conv
                 modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],
                                                           out_ch=filters,
-                                                          k=size,
+                                                          k=k,
                                                           stride=stride,
                                                           bias=not bn))
 
@@ -58,10 +58,10 @@ def create_modules(module_defs, img_size):
                 modules.running_var = torch.tensor([0.0524, 0.0502, 0.0506])
 
         elif mdef['type'] == 'maxpool':
-            size = mdef['size']
+            k = mdef['size']  # kernel size
             stride = mdef['stride']
-            maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=(size - 1) // 2)
-            if size == 2 and stride == 1:  # yolov3-tiny
+            maxpool = nn.MaxPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)
+            if k == 2 and stride == 1:  # yolov3-tiny
                 modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
                 modules.add_module('MaxPool2d', maxpool)
             else: