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Update YOLO11 Actions and Docs (#16596)

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docs/en/datasets/detect/voc.md
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@ -39,7 +39,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the VOC 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 model on the VOC 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"
@ -49,7 +49,7 @@ To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultraly
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
@ -59,7 +59,7 @@ To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultraly
```bash
# Start training from a pretrained *.pt model
yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -99,9 +99,9 @@ We would like to acknowledge the PASCAL VOC Consortium for creating and maintain
The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a renowned benchmark for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in computer vision. It includes comprehensive annotations like bounding boxes, class labels, and segmentation masks across 20 different object categories. Researchers use it widely to evaluate the performance of models like Faster R-CNN, YOLO, and Mask R-CNN due to its standardized evaluation metrics such as mean Average Precision (mAP).
### How do I train a YOLOv8 model using the VOC dataset?
### How do I train a YOLO11 model using the VOC dataset?
To train a YOLOv8 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLOv8n model for 100 epochs with an image size of 640:
To train a YOLO11 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLO11n model for 100 epochs with an image size of 640:
!!! example "Train Example"
@ -111,7 +111,7 @@ To train a YOLOv8 model with the VOC dataset, you need the dataset configuration
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
@ -121,7 +121,7 @@ To train a YOLOv8 model with the VOC dataset, you need the dataset configuration
```bash
# Start training from a pretrained *.pt model
yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
```
### What are the primary challenges included in the VOC dataset?