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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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docs/en/guides/isolating-segmentation-objects.md
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@ -1,7 +1,7 @@
---
comments: true
description: Learn to extract isolated objects from inference results using Ultralytics Predict Mode. Step-by-step guide for segmentation object isolation.
keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLOv8, machine learning, object detection, binary mask, image processing
keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLO11, machine learning, object detection, binary mask, image processing
---
# Isolating Segmentation Objects
@ -24,7 +24,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt")
model = YOLO("yolo11n-seg.pt")
# Run inference
results = model.predict()
@ -267,7 +267,7 @@ import numpy as np
from ultralytics import YOLO
m = YOLO("yolov8n-seg.pt") # (4)!
m = YOLO("yolo11n-seg.pt") # (4)!
res = m.predict() # (3)!
# Iterate detection results (5)
@ -310,16 +310,16 @@ for r in res:
## FAQ
### How do I isolate objects using Ultralytics YOLOv8 for segmentation tasks?
### How do I isolate objects using Ultralytics YOLO11 for segmentation tasks?
To isolate objects using Ultralytics YOLOv8, follow these steps:
To isolate objects using Ultralytics YOLO11, follow these steps:
1. **Load the model and run inference:**
```python
from ultralytics import YOLO
model = YOLO("yolov8n-seg.pt")
model = YOLO("yolo11n-seg.pt")
results = model.predict(source="path/to/your/image.jpg")
```
@ -345,7 +345,7 @@ Refer to the guide on [Predict Mode](../modes/predict.md) and the [Segment Task]
### What options are available for saving the isolated objects after segmentation?
Ultralytics YOLOv8 offers two main options for saving isolated objects:
Ultralytics YOLO11 offers two main options for saving isolated objects:
1. **With a Black Background:**
@ -361,7 +361,7 @@ Ultralytics YOLOv8 offers two main options for saving isolated objects:
For further details, visit the [Predict Mode](../modes/predict.md) section.
### How can I crop isolated objects to their bounding boxes using Ultralytics YOLOv8?
### How can I crop isolated objects to their bounding boxes using Ultralytics YOLO11?
To crop isolated objects to their bounding boxes:
@ -378,9 +378,9 @@ To crop isolated objects to their bounding boxes:
Learn more about bounding box results in the [Predict Mode](../modes/predict.md#boxes) documentation.
### Why should I use Ultralytics YOLOv8 for object isolation in segmentation tasks?
### Why should I use Ultralytics YOLO11 for object isolation in segmentation tasks?
Ultralytics YOLOv8 provides:
Ultralytics YOLO11 provides:
- **High-speed** real-time object detection and segmentation.
- **Accurate bounding box and mask generation** for precise object isolation.
@ -388,9 +388,9 @@ Ultralytics YOLOv8 provides:
Explore the benefits of using YOLO in the [Segment Task documentation](../tasks/segment.md).
### Can I save isolated objects including the background using Ultralytics YOLOv8?
### Can I save isolated objects including the background using Ultralytics YOLO11?
Yes, this is a built-in feature in Ultralytics YOLOv8. Use the `save_crop` argument in the `predict()` method. For example:
Yes, this is a built-in feature in Ultralytics YOLO11. Use the `save_crop` argument in the `predict()` method. For example:
```python
results = model.predict(source="path/to/your/image.jpg", save_crop=True)