diff --git a/ppocr/modeling/backbones/det_resnet_vd.py b/ppocr/modeling/backbones/det_resnet_vd.py
index 3bb4a0d50501860d5e9df2971e93fba66c152187..a29cf1b5e1ff56e59984bc91226ef7e6b65d0da1 100644
--- a/ppocr/modeling/backbones/det_resnet_vd.py
+++ b/ppocr/modeling/backbones/det_resnet_vd.py
@@ -25,16 +25,14 @@ __all__ = ["ResNet"]
 
 
 class ConvBNLayer(nn.Layer):
-    def __init__(
-            self,
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=1,
-            groups=1,
-            is_vd_mode=False,
-            act=None,
-            name=None, ):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 groups=1,
+                 is_vd_mode=False,
+                 act=None):
         super(ConvBNLayer, self).__init__()
 
         self.is_vd_mode = is_vd_mode
@@ -47,19 +45,8 @@ class ConvBNLayer(nn.Layer):
             stride=stride,
             padding=(kernel_size - 1) // 2,
             groups=groups,
-            weight_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        self._batch_norm = nn.BatchNorm(
-            out_channels,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
+        self._batch_norm = nn.BatchNorm(out_channels, act=act)
 
     def forward(self, inputs):
         if self.is_vd_mode:
@@ -75,29 +62,25 @@ class BottleneckBlock(nn.Layer):
                  out_channels,
                  stride,
                  shortcut=True,
-                 if_first=False,
-                 name=None):
+                 if_first=False):
         super(BottleneckBlock, self).__init__()
 
         self.conv0 = ConvBNLayer(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=1,
-            act='relu',
-            name=name + "_branch2a")
+            act='relu')
         self.conv1 = ConvBNLayer(
             in_channels=out_channels,
             out_channels=out_channels,
             kernel_size=3,
             stride=stride,
-            act='relu',
-            name=name + "_branch2b")
+            act='relu')
         self.conv2 = ConvBNLayer(
             in_channels=out_channels,
             out_channels=out_channels * 4,
             kernel_size=1,
-            act=None,
-            name=name + "_branch2c")
+            act=None)
 
         if not shortcut:
             self.short = ConvBNLayer(
@@ -105,8 +88,7 @@ class BottleneckBlock(nn.Layer):
                 out_channels=out_channels * 4,
                 kernel_size=1,
                 stride=1,
-                is_vd_mode=False if if_first else True,
-                name=name + "_branch1")
+                is_vd_mode=False if if_first else True)
 
         self.shortcut = shortcut
 
@@ -125,13 +107,13 @@ class BottleneckBlock(nn.Layer):
 
 
 class BasicBlock(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride,
-                 shortcut=True,
-                 if_first=False,
-                 name=None):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            stride,
+            shortcut=True,
+            if_first=False, ):
         super(BasicBlock, self).__init__()
         self.stride = stride
         self.conv0 = ConvBNLayer(
@@ -139,14 +121,12 @@ class BasicBlock(nn.Layer):
             out_channels=out_channels,
             kernel_size=3,
             stride=stride,
-            act='relu',
-            name=name + "_branch2a")
+            act='relu')
         self.conv1 = ConvBNLayer(
             in_channels=out_channels,
             out_channels=out_channels,
             kernel_size=3,
-            act=None,
-            name=name + "_branch2b")
+            act=None)
 
         if not shortcut:
             self.short = ConvBNLayer(
@@ -154,8 +134,7 @@ class BasicBlock(nn.Layer):
                 out_channels=out_channels,
                 kernel_size=1,
                 stride=1,
-                is_vd_mode=False if if_first else True,
-                name=name + "_branch1")
+                is_vd_mode=False if if_first else True)
 
         self.shortcut = shortcut
 
@@ -201,22 +180,19 @@ class ResNet(nn.Layer):
             out_channels=32,
             kernel_size=3,
             stride=2,
-            act='relu',
-            name="conv1_1")
+            act='relu')
         self.conv1_2 = ConvBNLayer(
             in_channels=32,
             out_channels=32,
             kernel_size=3,
             stride=1,
-            act='relu',
-            name="conv1_2")
+            act='relu')
         self.conv1_3 = ConvBNLayer(
             in_channels=32,
             out_channels=64,
             kernel_size=3,
             stride=1,
-            act='relu',
-            name="conv1_3")
+            act='relu')
         self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
 
         self.stages = []
@@ -226,13 +202,6 @@ class ResNet(nn.Layer):
                 block_list = []
                 shortcut = False
                 for i in range(depth[block]):
-                    if layers in [101, 152] and block == 2:
-                        if i == 0:
-                            conv_name = "res" + str(block + 2) + "a"
-                        else:
-                            conv_name = "res" + str(block + 2) + "b" + str(i)
-                    else:
-                        conv_name = "res" + str(block + 2) + chr(97 + i)
                     bottleneck_block = self.add_sublayer(
                         'bb_%d_%d' % (block, i),
                         BottleneckBlock(
@@ -241,8 +210,7 @@ class ResNet(nn.Layer):
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,
-                            if_first=block == i == 0,
-                            name=conv_name))
+                            if_first=block == i == 0))
                     shortcut = True
                     block_list.append(bottleneck_block)
                 self.out_channels.append(num_filters[block] * 4)
@@ -252,7 +220,6 @@ class ResNet(nn.Layer):
                 block_list = []
                 shortcut = False
                 for i in range(depth[block]):
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
                     basic_block = self.add_sublayer(
                         'bb_%d_%d' % (block, i),
                         BasicBlock(
@@ -261,8 +228,7 @@ class ResNet(nn.Layer):
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,
-                            if_first=block == i == 0,
-                            name=conv_name))
+                            if_first=block == i == 0))
                     shortcut = True
                     block_list.append(basic_block)
                 self.out_channels.append(num_filters[block])