diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index 94cb5af0..df136c14 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -1544,10 +1544,10 @@ class NMSModel(torch.nn.Module):
         Performs inference with NMS post-processing. Supports Detect, Segment, OBB and Pose.
 
         Args:
-            x (torch.tensor): The preprocessed tensor with shape (N, 3, H, W).
+            x (torch.Tensor): The preprocessed tensor with shape (N, 3, H, W).
 
         Returns:
-            out (torch.tensor): The post-processed results with shape (N, max_det, 4 + 2 + extra_shape).
+            out (torch.Tensor): The post-processed results with shape (N, max_det, 4 + 2 + extra_shape).
         """
         from functools import partial
 
diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 31f7349f..4ed8d7da 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -484,12 +484,6 @@ class RTDETRDetectionModel(DetectionModel):
     the training and inference processes. RTDETR is an object detection and tracking model that extends from the
     DetectionModel base class.
 
-    Attributes:
-        cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'.
-        ch (int): Number of input channels. Default is 3 (RGB).
-        nc (int, optional): Number of classes for object detection. Default is None.
-        verbose (bool): Specifies if summary statistics are shown during initialization. Default is True.
-
     Methods:
         init_criterion: Initializes the criterion used for loss calculation.
         loss: Computes and returns the loss during training.