diff --git a/docs/en/models/sam-2.md b/docs/en/models/sam-2.md
index d5e8888e..9083899e 100644
--- a/docs/en/models/sam-2.md
+++ b/docs/en/models/sam-2.md
@@ -250,13 +250,13 @@ To auto-annotate your dataset using SAM 2, follow this example:
     ```python
     from ultralytics.data.annotator import auto_annotate
 
-    auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam2_b.pt")
+    auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam2_b.pt")
     ```
 
 | Argument     | Type                    | Description                                                                                             | Default        |
 | ------------ | ----------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
 | `data`       | `str`                   | Path to a folder containing images to be annotated.                                                     |                |
-| `det_model`  | `str`, optional         | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.                                             | `'yolov8x.pt'` |
+| `det_model`  | `str`, optional         | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'.                                             | `'yolov8x.pt'` |
 | `sam_model`  | `str`, optional         | Pre-trained SAM 2 segmentation model. Defaults to 'sam2_b.pt'.                                          | `'sam2_b.pt'`  |
 | `device`     | `str`, optional         | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                    |                |
 | `output_dir` | `str`, `None`, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None`         |
diff --git a/docs/en/models/sam.md b/docs/en/models/sam.md
index f9acad72..c38b06e3 100644
--- a/docs/en/models/sam.md
+++ b/docs/en/models/sam.md
@@ -205,13 +205,13 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
         ```python
         from ultralytics.data.annotator import auto_annotate
 
-        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
+        auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt")
         ```
 
 | Argument     | Type                  | Description                                                                                             | Default        |
 | ------------ | --------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
 | `data`       | `str`                 | Path to a folder containing images to be annotated.                                                     |                |
-| `det_model`  | `str`, optional       | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.                                             | `'yolov8x.pt'` |
+| `det_model`  | `str`, optional       | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'.                                             | `'yolov8x.pt'` |
 | `sam_model`  | `str`, optional       | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.                                             | `'sam_b.pt'`   |
 | `device`     | `str`, optional       | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                    |                |
 | `output_dir` | `str`, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None`         |
diff --git a/ultralytics/data/annotator.py b/ultralytics/data/annotator.py
index 30d02d9d..3880741d 100644
--- a/ultralytics/data/annotator.py
+++ b/ultralytics/data/annotator.py
@@ -5,7 +5,7 @@ from pathlib import Path
 from ultralytics import SAM, YOLO
 
 
-def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="", output_dir=None):
+def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", output_dir=None):
     """
     Automatically annotates images using a YOLO object detection model and a SAM segmentation model.