diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py
index 33c724b518..1a8e8b9676 100644
--- a/ultralytics/nn/modules/head.py
+++ b/ultralytics/nn/modules/head.py
@@ -59,16 +59,17 @@ class Detect(nn.Module):
             cls = x_cat[:, self.reg_max * 4 :]
         else:
             box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
-        dbox = self.decode_bboxes(box)
 
         if self.export and self.format in ("tflite", "edgetpu"):
             # Precompute normalization factor to increase numerical stability
             # See https://github.com/ultralytics/ultralytics/issues/7371
-            img_h = shape[2]
-            img_w = shape[3]
-            img_size = torch.tensor([img_w, img_h, img_w, img_h], device=box.device).reshape(1, 4, 1)
-            norm = self.strides / (self.stride[0] * img_size)
-            dbox = dist2bbox(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2], xywh=True, dim=1)
+            grid_h = shape[2]
+            grid_w = shape[3]
+            grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
+            norm = self.strides / (self.stride[0] * grid_size)
+            dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
+        else:
+            dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
 
         y = torch.cat((dbox, cls.sigmoid()), 1)
         return y if self.export else (y, x)
@@ -82,9 +83,9 @@ class Detect(nn.Module):
             a[-1].bias.data[:] = 1.0  # box
             b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
 
-    def decode_bboxes(self, bboxes):
+    def decode_bboxes(self, bboxes, anchors):
         """Decode bounding boxes."""
-        return dist2bbox(self.dfl(bboxes), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        return dist2bbox(bboxes, anchors, xywh=True, dim=1)
 
 
 class Segment(Detect):
@@ -139,9 +140,9 @@ class OBB(Detect):
             return x, angle
         return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle))
 
-    def decode_bboxes(self, bboxes):
+    def decode_bboxes(self, bboxes, anchors):
         """Decode rotated bounding boxes."""
-        return dist2rbox(self.dfl(bboxes), self.angle, self.anchors.unsqueeze(0), dim=1) * self.strides
+        return dist2rbox(bboxes, self.angle, anchors, dim=1)
 
 
 class Pose(Detect):