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Update YOLO11 Actions and Docs (#16596)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
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10
docs/en/datasets/segment/index.md
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@ -74,7 +74,7 @@ The `train` and `val` fields specify the paths to the directories containing the
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
@ -84,7 +84,7 @@ The `train` and `val` fields specify the paths to the directories containing the
```bash
# Start training from a pretrained *.pt model
yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
```
## Supported Datasets
@ -137,13 +137,13 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
```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. | `None` |
| `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'`. | `'yolo11x.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 or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None` |
@ -195,7 +195,7 @@ Auto-annotation in Ultralytics YOLO allows you to generate segmentation annotati
```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")
```
This function automates the annotation process, making it faster and more efficient. For more details, explore the [Auto-Annotation](#auto-annotation) section.