diff --git a/models/common.py b/models/common.py
index 2c2d600..a23d73e 100644
--- a/models/common.py
+++ b/models/common.py
@@ -76,12 +76,6 @@ class SPP(nn.Module):
         return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
 
 
-class Flatten(nn.Module):
-    # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
-    def forward(self, x):
-        return x.view(x.size(0), -1)
-
-
 class Focus(nn.Module):
     # Focus wh information into c-space
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
@@ -100,3 +94,22 @@ class Concat(nn.Module):
 
     def forward(self, x):
         return torch.cat(x, self.d)
+
+
+class Flatten(nn.Module):
+    # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
+    @staticmethod
+    def forward(x):
+        return x.view(x.size(0), -1)
+
+
+class Classify(nn.Module):
+    # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Classify, self).__init__()
+        self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)  # to x(b,c2,1,1)
+        self.flat = Flatten()
+
+    def forward(self, x):
+        return self.flat(self.conv(self.aap(x)))  # flatten to x(b,c2)