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Update `sam.md` and `sam-2.md` (#17286)

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Muhammad Rizwan Munawar 4 weeks ago committed by GitHub
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  1. 4
      docs/en/models/sam-2.md
  2. 4
      docs/en/models/sam.md
  3. 2
      ultralytics/data/annotator.py

4
docs/en/models/sam-2.md
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@ -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` |

4
docs/en/models/sam.md
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@ -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` |

2
ultralytics/data/annotator.py
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@ -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.

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