diff --git a/models/yolo.py b/models/yolo.py
index cf5ccad..ee5e5a7 100644
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -522,16 +522,16 @@ class Model(nn.Module):
         if isinstance(m, Detect):
             s = 256  # 2x min stride
             m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
             check_anchor_order(m)
+            m.anchors /= m.stride.view(-1, 1, 1)
             self.stride = m.stride
             self._initialize_biases()  # only run once
             # print('Strides: %s' % m.stride.tolist())
         if isinstance(m, IDetect):
             s = 256  # 2x min stride
             m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
             check_anchor_order(m)
+            m.anchors /= m.stride.view(-1, 1, 1)
             self.stride = m.stride
             self._initialize_biases()  # only run once
             # print('Strides: %s' % m.stride.tolist())
@@ -539,24 +539,24 @@ class Model(nn.Module):
             s = 256  # 2x min stride
             m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))[:4]])  # forward
             #print(m.stride)
-            m.anchors /= m.stride.view(-1, 1, 1)
             check_anchor_order(m)
+            m.anchors /= m.stride.view(-1, 1, 1)
             self.stride = m.stride
             self._initialize_aux_biases()  # only run once
             # print('Strides: %s' % m.stride.tolist())
         if isinstance(m, IBin):
             s = 256  # 2x min stride
             m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
             check_anchor_order(m)
+            m.anchors /= m.stride.view(-1, 1, 1)
             self.stride = m.stride
             self._initialize_biases_bin()  # only run once
             # print('Strides: %s' % m.stride.tolist())
         if isinstance(m, IKeypoint):
             s = 256  # 2x min stride
             m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
             check_anchor_order(m)
+            m.anchors /= m.stride.view(-1, 1, 1)
             self.stride = m.stride
             self._initialize_biases_kpt()  # only run once
             # print('Strides: %s' % m.stride.tolist())