diff --git a/torchvision/models/detection/_utils.py b/torchvision/models/detection/_utils.py
index f2d4d4eb337..36e99e6506d 100644
--- a/torchvision/models/detection/_utils.py
+++ b/torchvision/models/detection/_utils.py
@@ -470,7 +470,12 @@ def retrieve_out_channels(model: nn.Module, size: Tuple[int, int]) -> List[int]:
     return out_channels
 
 
-def _topk_min(input: Tensor, orig_kval: int, axis: int) -> Tensor:
+@torch.jit.unused
+def _fake_cast_onnx(v: Tensor) -> int:
+    return v  # type: ignore[return-value]
+
+
+def _topk_min(input: Tensor, orig_kval: int, axis: int) -> int:
     """
     ONNX spec requires the k-value to be less than or equal to the number of inputs along
     provided dim. Certain models use the number of elements along a particular axis instead of K
@@ -487,8 +492,10 @@ def _topk_min(input: Tensor, orig_kval: int, axis: int) -> Tensor:
         axis(int): Axis along which we retreive the input size.
 
     Returns:
-        min_kval (Tensor): Appropriately selected k-value.
+        min_kval (int): Appropriately selected k-value.
     """
+    if not torch.jit.is_tracing():
+        return min(orig_kval, input.size(axis))
     axis_dim_val = torch._shape_as_tensor(input)[axis].unsqueeze(0)
     min_kval = torch.min(torch.cat((torch.tensor([orig_kval], dtype=axis_dim_val.dtype), axis_dim_val), 0))
-    return min_kval  # type: ignore[arg-type]
+    return _fake_cast_onnx(min_kval)