diff --git a/torchvision/models/detection/_utils.py b/torchvision/models/detection/_utils.py
index ef4f6550eef..f2d4d4eb337 100644
--- a/torchvision/models/detection/_utils.py
+++ b/torchvision/models/detection/_utils.py
@@ -468,3 +468,27 @@ def retrieve_out_channels(model: nn.Module, size: Tuple[int, int]) -> List[int]:
         model.train()
 
     return out_channels
+
+
+def _topk_min(input: Tensor, orig_kval: int, axis: int) -> Tensor:
+    """
+    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
+    if K exceeds the number of elements along that axis. Previously, python's min() function was
+    used to determine whether to use the provided k-value or the specified dim axis value.
+
+    However in cases where the model is being exported in tracing mode, python min() is
+    static causing the model to be traced incorrectly and eventually fail at the topk node.
+    In order to avoid this situation, in tracing mode, torch.min() is used instead.
+
+    Args:
+        input (Tensor): The orignal input tensor.
+        orig_kval (int): The provided k-value.
+        axis(int): Axis along which we retreive the input size.
+
+    Returns:
+        min_kval (Tensor): Appropriately selected k-value.
+    """
+    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]
diff --git a/torchvision/models/detection/fcos.py b/torchvision/models/detection/fcos.py
index 91baf1d0b29..32a413e9cb1 100644
--- a/torchvision/models/detection/fcos.py
+++ b/torchvision/models/detection/fcos.py
@@ -501,7 +501,7 @@ def postprocess_detections(
                 topk_idxs = torch.where(keep_idxs)[0]
 
                 # keep only topk scoring predictions
-                num_topk = min(self.topk_candidates, topk_idxs.size(0))
+                num_topk = det_utils._topk_min(topk_idxs, self.topk_candidates, 0)
                 scores_per_level, idxs = scores_per_level.topk(num_topk)
                 topk_idxs = topk_idxs[idxs]
 
diff --git a/torchvision/models/detection/retinanet.py b/torchvision/models/detection/retinanet.py
index e5ced9870ba..1909f6a8b73 100644
--- a/torchvision/models/detection/retinanet.py
+++ b/torchvision/models/detection/retinanet.py
@@ -436,7 +436,7 @@ def postprocess_detections(self, head_outputs, anchors, image_shapes):
                 topk_idxs = torch.where(keep_idxs)[0]
 
                 # keep only topk scoring predictions
-                num_topk = min(self.topk_candidates, topk_idxs.size(0))
+                num_topk = det_utils._topk_min(topk_idxs, self.topk_candidates, 0)
                 scores_per_level, idxs = scores_per_level.topk(num_topk)
                 topk_idxs = topk_idxs[idxs]
 
diff --git a/torchvision/models/detection/rpn.py b/torchvision/models/detection/rpn.py
index 15cec706fbb..1d63bcc8a54 100644
--- a/torchvision/models/detection/rpn.py
+++ b/torchvision/models/detection/rpn.py
@@ -1,7 +1,6 @@
-from typing import List, Optional, Dict, Tuple, cast
+from typing import List, Optional, Dict, Tuple
 
 import torch
-import torchvision
 from torch import nn, Tensor
 from torch.nn import functional as F
 from torchvision.ops import boxes as box_ops
@@ -13,17 +12,6 @@
 from .image_list import ImageList
 
 
-@torch.jit.unused
-def _onnx_get_num_anchors_and_pre_nms_top_n(ob: Tensor, orig_pre_nms_top_n: int) -> Tuple[int, int]:
-    from torch.onnx import operators
-
-    num_anchors = operators.shape_as_tensor(ob)[1].unsqueeze(0)
-    pre_nms_top_n = torch.min(torch.cat((torch.tensor([orig_pre_nms_top_n], dtype=num_anchors.dtype), num_anchors), 0))
-
-    # for mypy we cast at runtime
-    return cast(int, num_anchors), cast(int, pre_nms_top_n)
-
-
 class RPNHead(nn.Module):
     """
     Adds a simple RPN Head with classification and regression heads
@@ -206,11 +194,8 @@ def _get_top_n_idx(self, objectness: Tensor, num_anchors_per_level: List[int]) -
         r = []
         offset = 0
         for ob in objectness.split(num_anchors_per_level, 1):
-            if torchvision._is_tracing():
-                num_anchors, pre_nms_top_n = _onnx_get_num_anchors_and_pre_nms_top_n(ob, self.pre_nms_top_n())
-            else:
-                num_anchors = ob.shape[1]
-                pre_nms_top_n = min(self.pre_nms_top_n(), num_anchors)
+            num_anchors = ob.shape[1]
+            pre_nms_top_n = det_utils._topk_min(ob, self.pre_nms_top_n(), 1)
             _, top_n_idx = ob.topk(pre_nms_top_n, dim=1)
             r.append(top_n_idx + offset)
             offset += num_anchors
diff --git a/torchvision/models/detection/ssd.py b/torchvision/models/detection/ssd.py
index 5778a07075d..08a9ed68e4e 100644
--- a/torchvision/models/detection/ssd.py
+++ b/torchvision/models/detection/ssd.py
@@ -407,7 +407,7 @@ def postprocess_detections(
                 box = boxes[keep_idxs]
 
                 # keep only topk scoring predictions
-                num_topk = min(self.topk_candidates, score.size(0))
+                num_topk = det_utils._topk_min(score, self.topk_candidates, 0)
                 score, idxs = score.topk(num_topk)
                 box = box[idxs]