diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index d25836a01a6..2fa99db357a 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.34"
+__version__ = "8.1.35"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld
diff --git a/ultralytics/nn/modules/block.py b/ultralytics/nn/modules/block.py
index a263b603564..ca991f6e090 100644
--- a/ultralytics/nn/modules/block.py
+++ b/ultralytics/nn/modules/block.py
@@ -171,10 +171,9 @@ def __init__(self, c1, c2, k=5):
 
     def forward(self, x):
         """Forward pass through Ghost Convolution block."""
-        x = self.cv1(x)
-        y1 = self.m(x)
-        y2 = self.m(y1)
-        return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
+        y = [self.cv1(x)]
+        y.extend(self.m(y[-1]) for _ in range(3))
+        return self.cv2(torch.cat(y, 1))
 
 
 class C1(nn.Module):
@@ -555,40 +554,27 @@ def forward(self, x, w):
         return x * self.logit_scale.exp() + self.bias
 
 
-class RepBottleneck(nn.Module):
+class RepBottleneck(Bottleneck):
     """Rep bottleneck."""
 
     def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
         """Initializes a RepBottleneck module with customizable in/out channels, shortcut option, groups and expansion
         ratio.
         """
-        super().__init__()
+        super().__init__(c1, c2, shortcut, g, k, e)
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = RepConv(c1, c_, k[0], 1)
-        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        """Forward pass through RepBottleneck layer."""
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
 
 
-class RepCSP(nn.Module):
+class RepCSP(C3):
     """Rep CSP Bottleneck with 3 convolutions."""
 
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes RepCSP layer with given channels, repetitions, shortcut, groups and expansion ratio."""
-        super().__init__()
+        super().__init__(c1, c2, n, shortcut, g, e)
         c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
         self.m = nn.Sequential(*(RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
 
-    def forward(self, x):
-        """Forward pass through RepCSP layer."""
-        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
-
 
 class RepNCSPELAN4(nn.Module):
     """CSP-ELAN."""