diff --git a/ppocr/modeling/necks/db_fpn.py b/ppocr/modeling/necks/db_fpn.py
index 47a258e2a821366ef977251d47eb05bfeb96b45e..1c81be4a6246287ea8d03acb6efe75b71a73f466 100644
--- a/ppocr/modeling/necks/db_fpn.py
+++ b/ppocr/modeling/necks/db_fpn.py
@@ -39,7 +39,8 @@ class DSConv(nn.Layer):
                  stride=1,
                  groups=None,
                  if_act=True,
-                 act="relu"):
+                 act="relu",
+                 **kwargs):
         super(DSConv, self).__init__()
         if groups == None:
             groups = in_channels
@@ -263,7 +264,7 @@ class CAFPN(nn.Layer):
 
 
 class FEPAN(nn.Layer):
-    def __init__(self, in_channels, out_channels, **kwargs):
+    def __init__(self, in_channels, out_channels, mode='large', **kwargs):
         super(FEPAN, self).__init__()
         self.out_channels = out_channels
         weight_attr = paddle.nn.initializer.KaimingUniform()
@@ -274,6 +275,15 @@ class FEPAN(nn.Layer):
         self.pan_head_conv = nn.LayerList()
         self.pan_lat_conv = nn.LayerList()
 
+        if mode.lower() == 'lite':
+            p_layer = DSConv
+        elif mode.lower() == 'large':
+            p_layer = nn.Conv2D
+        else:
+            raise ValueError(
+                "mode can only be one of ['lite', 'large'], but received {}".
+                format(mode))
+
         for i in range(len(in_channels)):
             self.ins_conv.append(
                 nn.Conv2D(
@@ -284,7 +294,7 @@ class FEPAN(nn.Layer):
                     bias_attr=False))
 
             self.inp_conv.append(
-                nn.Conv2D(
+                p_layer(
                     in_channels=self.out_channels,
                     out_channels=self.out_channels // 4,
                     kernel_size=9,
@@ -303,7 +313,7 @@ class FEPAN(nn.Layer):
                         weight_attr=ParamAttr(initializer=weight_attr),
                         bias_attr=False))
             self.pan_lat_conv.append(
-                nn.Conv2D(
+                p_layer(
                     in_channels=self.out_channels // 4,
                     out_channels=self.out_channels // 4,
                     kernel_size=9,
@@ -346,86 +356,3 @@ class FEPAN(nn.Layer):
 
         fuse = paddle.concat([p5, p4, p3, p2], axis=1)
         return fuse
-
-
-class FEPANLite(nn.Layer):
-    def __init__(self, in_channels, out_channels, **kwargs):
-        super(FEPANLite, self).__init__()
-        self.out_channels = out_channels
-        weight_attr = paddle.nn.initializer.KaimingUniform()
-
-        self.ins_conv = nn.LayerList()
-        self.inp_conv = nn.LayerList()
-        # pan head
-        self.pan_head_conv = nn.LayerList()
-        self.pan_lat_conv = nn.LayerList()
-
-        for i in range(len(in_channels)):
-            self.ins_conv.append(
-                nn.Conv2D(
-                    in_channels=in_channels[i],
-                    out_channels=self.out_channels,
-                    kernel_size=1,
-                    weight_attr=ParamAttr(initializer=weight_attr),
-                    bias_attr=False))
-
-            self.inp_conv.append(
-                DSConv(
-                    in_channels=self.out_channels,
-                    out_channels=self.out_channels // 4,
-                    kernel_size=9,
-                    padding=4))
-
-            if i > 0:
-                self.pan_head_conv.append(
-                    nn.Conv2D(
-                        in_channels=self.out_channels // 4,
-                        out_channels=self.out_channels // 4,
-                        kernel_size=3,
-                        padding=1,
-                        stride=2,
-                        weight_attr=ParamAttr(initializer=weight_attr),
-                        bias_attr=False))
-
-            self.pan_lat_conv.append(
-                DSConv(
-                    in_channels=self.out_channels // 4,
-                    out_channels=self.out_channels // 4,
-                    kernel_size=9,
-                    padding=4))
-
-    def forward(self, x):
-        c2, c3, c4, c5 = x
-
-        in5 = self.ins_conv[3](c5)
-        in4 = self.ins_conv[2](c4)
-        in3 = self.ins_conv[1](c3)
-        in2 = self.ins_conv[0](c2)
-
-        out4 = in4 + F.upsample(
-            in5, scale_factor=2, mode="nearest", align_mode=1)  # 1/16
-        out3 = in3 + F.upsample(
-            out4, scale_factor=2, mode="nearest", align_mode=1)  # 1/8
-        out2 = in2 + F.upsample(
-            out3, scale_factor=2, mode="nearest", align_mode=1)  # 1/4
-
-        f5 = self.inp_conv[3](in5)
-        f4 = self.inp_conv[2](out4)
-        f3 = self.inp_conv[1](out3)
-        f2 = self.inp_conv[0](out2)
-
-        pan3 = f3 + self.pan_head_conv[0](f2)
-        pan4 = f4 + self.pan_head_conv[1](pan3)
-        pan5 = f5 + self.pan_head_conv[2](pan4)
-
-        p2 = self.pan_lat_conv[0](f2)
-        p3 = self.pan_lat_conv[1](pan3)
-        p4 = self.pan_lat_conv[2](pan4)
-        p5 = self.pan_lat_conv[3](pan5)
-
-        p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
-        p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
-        p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
-
-        fuse = paddle.concat([p5, p4, p3, p2], axis=1)
-        return fuse