diff --git a/ultralytics/yolo/engine/exporter.py b/ultralytics/yolo/engine/exporter.py
index dac2fcf9..7fa6cf40 100644
--- a/ultralytics/yolo/engine/exporter.py
+++ b/ultralytics/yolo/engine/exporter.py
@@ -59,7 +59,7 @@ from pathlib import Path
 import torch
 
 from ultralytics.nn.autobackend import check_class_names
-from ultralytics.nn.modules import C2f, Detect, Segment
+from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.utils import (DEFAULT_CFG, LINUX, LOGGER, MACOS, __version__, callbacks, colorstr,
@@ -157,13 +157,13 @@ class Exporter:
 
         # Load PyTorch model
         self.device = select_device('cpu' if self.args.device is None else self.args.device)
+
+        # Checks
+        model.names = check_class_names(model.names)
         if self.args.half and onnx and self.device.type == 'cpu':
             LOGGER.warning('WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0')
             self.args.half = False
             assert not self.args.dynamic, 'half=True not compatible with dynamic=True, i.e. use only one.'
-
-        # Checks
-        model.names = check_class_names(model.names)
         self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
         if self.args.optimize:
             assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
@@ -185,7 +185,7 @@ class Exporter:
         model.float()
         model = model.fuse()
         for k, m in model.named_modules():
-            if isinstance(m, (Detect, Segment)):
+            if isinstance(m, (Detect, RTDETRDecoder)):  # Segment and Pose use Detect base class
                 m.dynamic = self.args.dynamic
                 m.export = True
                 m.format = self.args.format