diff --git a/ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py b/ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py
index 5ef34e2dbee0471e71116d558a9040ea041202f1..750b488ff941e8b2e2624a7e704345f9ca690920 100644
--- a/ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py
+++ b/ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py
@@ -256,10 +256,10 @@ class PPLCNetV2(TheseusLayer):
                  depths=[2, 2, 6, 2],
                  class_num=1000,
                  dropout_prob=0.2,
-                 class_expand=1280):
+                 expand=1280):
         super().__init__()
         self.scale = scale
-        self.class_expand = class_expand
+        self.expand = expand
 
         self.stem = nn.Sequential(* [
             ConvBNLayer(
@@ -339,19 +339,22 @@ class PPLCNetV2(TheseusLayer):
 
         self.avg_pool = AdaptiveAvgPool2D(1)
 
-        self.last_conv = Conv2D(
-            in_channels=make_divisible(NET_CONFIG["stage4"][0] * 2 * scale),
-            out_channels=self.class_expand,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias_attr=False)
+        if self.expand:
+            self.last_conv = Conv2D(
+                in_channels=make_divisible(NET_CONFIG["stage4"][0] * 2 *
+                                           scale),
+                out_channels=self.expand,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias_attr=False)
+            self.act = nn.ReLU()
+            self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
 
-        self.act = nn.ReLU()
-        self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
         self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
-
-        self.fc = Linear(self.class_expand, class_num)
+        in_features = self.expand if self.expand else NET_CONFIG["stage4"][
+            0] * 2 * scale
+        self.fc = Linear(in_features, class_num)
 
     def forward(self, x):
         x = self.stem(x)
@@ -360,9 +363,10 @@ class PPLCNetV2(TheseusLayer):
         x = self.stage3(x)
         x = self.stage4(x)
         x = self.avg_pool(x)
-        x = self.last_conv(x)
-        x = self.act(x)
-        x = self.dropout(x)
+        if self.expand:
+            x = self.last_conv(x)
+            x = self.act(x)
+            x = self.dropout(x)
         x = self.flatten(x)
         x = self.fc(x)
         return x
@@ -393,4 +397,4 @@ def PPLCNetV2_base(pretrained=False, use_ssld=False, **kwargs):
     """
     model = PPLCNetV2(scale=1.0, depths=[2, 2, 6, 2], **kwargs)
     _load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
-    return model
\ No newline at end of file
+    return model