Update YOLO11 Actions and Docs (#16596)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/segment/coco8-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Discover the versatile and manageable COCO8-Seg dataset by Ultralytics, ideal for testing and debugging segmentation models or new detection approaches.
-keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model training, computer vision, dataset configuration
+keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLO11, COCO 2017, model training, computer vision, dataset configuration
 ---
 
 # COCO8-Seg Dataset
@@ -10,7 +10,7 @@ keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model
 
 [Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small, but versatile [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
 ## Dataset YAML
 
@@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -34,7 +34,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://w
         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)
@@ -44,7 +44,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://w
 
         ```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
         ```
 
 ## Sample Images and Annotations
@@ -80,13 +80,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
 
 ## FAQ
 
-### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLOv8?
+### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLO11?
 
-The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
+The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO11](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
 
-### How can I train a YOLOv8n-seg model using the COCO8-Seg dataset?
+### How can I train a YOLO11n-seg model using the COCO8-Seg dataset?
 
-To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
+To train a **YOLO11n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
 
 !!! example "Train Example"
 
@@ -96,7 +96,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
         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)
@@ -106,7 +106,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
 
         ```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
         ```
 
 For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.