diff --git a/kornia/models/detector/rtdetr.py b/kornia/models/detector/rtdetr.py
index cabf060027..d16bb9e372 100644
--- a/kornia/models/detector/rtdetr.py
+++ b/kornia/models/detector/rtdetr.py
@@ -2,7 +2,7 @@
 from typing import Optional
 
 import torch
-import torch.nn as nn
+from torch import nn
 
 from kornia.contrib.models.rt_detr import DETRPostProcessor
 from kornia.contrib.models.rt_detr.model import RTDETR, RTDETRConfig
@@ -66,7 +66,7 @@ def build(
         return ObjectDetector(
             model,
             ResizePreProcessor(image_size) if image_size is not None else nn.Identity(),
-            DETRPostProcessor(confidence_threshold)
+            DETRPostProcessor(confidence_threshold),
         )
 
     @staticmethod
@@ -119,16 +119,13 @@ def to_onnx(
 
         if image_size is None:
             val_image = rand(1, 3, 640, 640)
-            dynamic_axes={
-                'input' : {0 : 'batch_size', 2: 'height', 3: 'width'},
-                'output' : {0 : 'batch_size', 2: 'height', 3: 'width'}
+            dynamic_axes = {
+                "input": {0: "batch_size", 2: "height", 3: "width"},
+                "output": {0: "batch_size", 2: "height", 3: "width"},
             }
         else:
             val_image = rand(1, 3, image_size, image_size)
-            dynamic_axes={
-                'input' : {0 : 'batch_size'},
-                'output' : {0 : 'batch_size'}
-            }
+            dynamic_axes = {"input": {0: "batch_size"}, "output": {0: "batch_size"}}
         torch.onnx.export(
             detector,
             val_image,
@@ -136,7 +133,7 @@ def to_onnx(
             export_params=True,
             opset_version=17,
             do_constant_folding=True,
-            input_names=['input'],
-            output_names=['output'],
-            dynamic_axes=dynamic_axes
+            input_names=["input"],
+            output_names=["output"],
+            dynamic_axes=dynamic_axes,
         )