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Update YOLO11 Actions and Docs (#16596)

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docs/en/datasets/segment/crack-seg.md
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@ -34,7 +34,7 @@ A YAML (Yet Another Markup Language) file is employed to outline the configurati
## Usage
To train Ultralytics YOLOv8n model on the Crack Segmentation 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 Ultralytics YOLO11n model on the Crack Segmentation 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"
@ -44,7 +44,7 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [ep
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="crack-seg.yaml", epochs=100, imgsz=640)
@ -54,7 +54,7 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [ep
```bash
# Start training from a pretrained *.pt model
yolo segment train data=crack-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -98,9 +98,9 @@ We would like to acknowledge the Roboflow team for creating and maintaining the
The [Roboflow Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) is a comprehensive collection of 4029 static images designed specifically for transportation and public safety studies. It is ideal for tasks such as self-driving car model development and infrastructure maintenance. The dataset includes training, testing, and validation sets, aiding in accurate crack detection and segmentation.
### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLOv8?
### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLO11?
To train an Ultralytics YOLOv8 model on the Crack Segmentation dataset, use the following code snippets. Detailed instructions and further parameters can be found on the model [Training](../../modes/train.md) page.
To train an Ultralytics YOLO11 model on the Crack Segmentation dataset, use the following code snippets. Detailed instructions and further parameters can be found on the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -110,7 +110,7 @@ To train an Ultralytics YOLOv8 model on the Crack Segmentation dataset, use 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="crack-seg.yaml", epochs=100, imgsz=640)
@ -120,7 +120,7 @@ To train an Ultralytics YOLOv8 model on the Crack Segmentation dataset, use the
```bash
# Start training from a pretrained *.pt model
yolo segment train data=crack-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
```
### Why should I use the Crack Segmentation Dataset for my self-driving car project?