diff --git a/kornia/contrib/models/rt_detr/post_processor.py b/kornia/contrib/models/rt_detr/post_processor.py
index 95ad6109c7..0ae898ad5e 100644
--- a/kornia/contrib/models/rt_detr/post_processor.py
+++ b/kornia/contrib/models/rt_detr/post_processor.py
@@ -1,11 +1,35 @@
+"""Post-processor for the RT-DETR model."""
+
 from __future__ import annotations
 
-# TODO:
 import torch
 
 from kornia.core import Module, Tensor, concatenate
 
 
+def mod(a, b):
+    """Compute the modulo operation for two numbers.
+
+    This function calculates the remainder of the division of 'a' by 'b'
+    using the formula: a - (a // b) * b, which is equivalent to the modulo operation.
+
+    Args:
+        a: The dividend.
+        b: The divisor.
+
+    Returns:
+        The remainder of a divided by b.
+
+    Example:
+        >>> mod(7, 3)
+        1
+        >>> mod(8.5, 3.2)
+        2.1
+    """
+    return a - (a // b) * b
+
+
+# TODO: deprecate the confidence threshold and add the num_top_queries as a parameter and num_classes as a parameter
 class DETRPostProcessor(Module):
     def __init__(self, confidence_threshold: float) -> None:
         super().__init__()
@@ -45,16 +69,13 @@ def forward(self, logits: Tensor, boxes: Tensor, original_sizes: Tensor) -> list
         boxes_xy = boxes_xy * sizes_wh
         scores = logits.sigmoid()  # RT-DETR was trained with focal loss. thus sigmoid is used instead of softmax
 
-        # the original code is slightly different
-        # it allows 1 bounding box to have multiple classes (multi-label)
-        scores, labels = scores.max(-1)
-
-        detections: list[Tensor] = []
-        for i in range(scores.shape[0]):
-            mask = scores[i] >= self.confidence_threshold
-            labels_i = labels[i, mask].unsqueeze(-1)
-            scores_i = scores[i, mask].unsqueeze(-1)
-            boxes_i = boxes_xy[i, mask]
-            detections.append(concatenate([labels_i, scores_i, boxes_i], -1))
+        # retrieve the boxes with the highest score for each class
+        # https://github.com/lyuwenyu/RT-DETR/blob/b6bf0200b249a6e35b44e0308b6058f55b99696b/rtdetrv2_pytorch/src/zoo/rtdetr/rtdetr_postprocessor.py#L55-L62
+        num_top_queries = 300  # TODO: make this configurable
+        num_classes = 80  # TODO: make this configurable
+        scores, index = torch.topk(scores.flatten(1), num_top_queries, dim=-1)
+        labels = mod(index, num_classes)
+        index = index // num_classes
+        boxes = boxes_xy.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes_xy.shape[-1]))
 
-        return detections
+        return concatenate([labels[..., None], scores[..., None], boxes], -1)
diff --git a/kornia/contrib/object_detection.py b/kornia/contrib/object_detection.py
index b348899a3b..9a289b4f17 100644
--- a/kornia/contrib/object_detection.py
+++ b/kornia/contrib/object_detection.py
@@ -12,6 +12,7 @@
 from kornia.core.check import KORNIA_CHECK_SHAPE
 from kornia.core.external import PILImage as Image
 from kornia.core.external import numpy as np
+from kornia.geometry.transform import resize
 from kornia.io import write_image
 from kornia.utils.draw import draw_rectangle
 
@@ -126,13 +127,12 @@ def forward(self, imgs: list[Tensor]) -> tuple[Tensor, Tensor]:
         """
         # TODO: support other input formats e.g. file path, numpy
         resized_imgs, original_sizes = [], []
-        for i in range(len(imgs)):
+        for i in range(imgs.shape[0]):
             img = imgs[i]
-            # NOTE: assume that image layout is CHW
             original_sizes.append([img.shape[-2], img.shape[-1]])
             resized_imgs.append(
-                # TODO: fix kornia resize to support onnx
-                torch.nn.functional.interpolate(img.unsqueeze(0), size=self.size, mode=self.interpolation_mode)
+                # TODO: fix kornia resize warnings
+                resize(img[None], size=self.size, interpolation=self.interpolation_mode)
             )
         return concatenate(resized_imgs), as_tensor(original_sizes)
 
@@ -181,7 +181,8 @@ def draw(self, images: list[Tensor], output_type: str = "torch") -> list[Tensor]
             out_img = image[None].clone()
             for out in detection:
                 out_img = draw_rectangle(
-                    out_img, torch.Tensor([[[out[-4], out[-3], out[-4] + out[-2], out[-3] + out[-1]]]])
+                    out_img,
+                    torch.Tensor([[[out[-4], out[-3], out[-4] + out[-2], out[-3] + out[-1]]]]),
                 )
             if output_type == "torch":
                 output.append(out_img[0])
@@ -204,7 +205,10 @@ def save(self, images: list[Tensor], directory: Optional[str] = None) -> None:
         outputs = self.draw(images)
         os.makedirs(directory, exist_ok=True)
         for i, out_image in enumerate(outputs):
-            write_image(os.path.join(directory, f"{str(i).zfill(6)}.jpg"), out_image.mul(255.0).byte())
+            write_image(
+                os.path.join(directory, f"{str(i).zfill(6)}.jpg"),
+                out_image.mul(255.0).byte(),
+            )
 
     def compile(
         self,