Update YOLO11 Actions and Docs (#16596)

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

docs/en/guides/preprocessing_annotated_data.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Learn essential data preprocessing techniques for annotated computer vision data, including resizing, normalizing, augmenting, and splitting datasets for optimal model training.
-keywords: data preprocessing, computer vision, image resizing, normalization, data augmentation, training dataset, validation dataset, test dataset, YOLOv8
+keywords: data preprocessing, computer vision, image resizing, normalization, data augmentation, training dataset, validation dataset, test dataset, YOLO11
 ---
 
 # Data Preprocessing Techniques for Annotated [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) Data
@@ -36,7 +36,7 @@ To make resizing a simpler task, you can use the following tools:
 - **[OpenCV](https://www.ultralytics.com/glossary/opencv)**: A popular computer vision library with extensive functions for image processing.
 - **PIL (Pillow)**: A Python Imaging Library for opening, manipulating, and saving image files.
 
-With respect to YOLOv8, the 'imgsz' parameter during [model training](../modes/train.md) allows for flexible input sizes. When set to a specific size, such as 640, the model will resize input images so their largest dimension is 640 pixels while maintaining the original aspect ratio.
+With respect to YOLO11, the 'imgsz' parameter during [model training](../modes/train.md) allows for flexible input sizes. When set to a specific size, such as 640, the model will resize input images so their largest dimension is 640 pixels while maintaining the original aspect ratio.
 
 By evaluating your model's and dataset's specific needs, you can determine whether resizing is a necessary preprocessing step or if your model can efficiently handle images of varying sizes.
 
@@ -47,7 +47,7 @@ Another preprocessing technique is normalization. Normalization scales the pixel
 - **Min-Max Scaling**: Scales pixel values to a range of 0 to 1.
 - **Z-Score Normalization**: Scales pixel values based on their mean and standard deviation.
 
-With respect to YOLOv8, normalization is seamlessly handled as part of its preprocessing pipeline during model training. YOLOv8 automatically performs several preprocessing steps, including conversion to RGB, scaling pixel values to the range [0, 1], and normalization using predefined mean and standard deviation values.
+With respect to YOLO11, normalization is seamlessly handled as part of its preprocessing pipeline during model training. YOLO11 automatically performs several preprocessing steps, including conversion to RGB, scaling pixel values to the range [0, 1], and normalization using predefined mean and standard deviation values.
 
 ### Splitting the Dataset
 
@@ -76,9 +76,9 @@ Common augmentation techniques include flipping, rotation, scaling, and color ad
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/overview-of-data-augmentations.avif" alt="Overview of Data Augmentations">
 </p>
 
-With respect to YOLOv8, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data.
+With respect to YOLO11, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data.
 
-The [Ultralytics YOLOv8 repository](https://github.com/ultralytics/ultralytics/tree/main) supports a wide range of data augmentations. You can apply various transformations such as:
+The [Ultralytics YOLO11 repository](https://github.com/ultralytics/ultralytics/tree/main) supports a wide range of data augmentations. You can apply various transformations such as:
 
 - Random Crops
 - Flipping: Images can be flipped horizontally or vertically.
@@ -89,12 +89,12 @@ Also, you can adjust the intensity of these augmentation techniques through spec
 
 ## A Case Study of Preprocessing
 
-Consider a project aimed at developing a model to detect and classify different types of vehicles in traffic images using YOLOv8. We've collected traffic images and annotated them with bounding boxes and labels.
+Consider a project aimed at developing a model to detect and classify different types of vehicles in traffic images using YOLO11. We've collected traffic images and annotated them with bounding boxes and labels.
 
 Here's what each step of preprocessing would look like for this project:
 
-- Resizing Images: Since YOLOv8 handles flexible input sizes and performs resizing automatically, manual resizing is not required. The model will adjust the image size according to the specified 'imgsz' parameter during training.
-- Normalizing Pixel Values: YOLOv8 automatically normalizes pixel values to a range of 0 to 1 during preprocessing, so it's not required.
+- Resizing Images: Since YOLO11 handles flexible input sizes and performs resizing automatically, manual resizing is not required. The model will adjust the image size according to the specified 'imgsz' parameter during training.
+- Normalizing Pixel Values: YOLO11 automatically normalizes pixel values to a range of 0 to 1 during preprocessing, so it's not required.
 - Splitting the Dataset: Divide the dataset into training (70%), validation (20%), and test (10%) sets using tools like scikit-learn.
 - [Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation): Modify the dataset configuration file (.yaml) to include data augmentation techniques such as random crops, horizontal flips, and brightness adjustments.
 
@@ -132,12 +132,12 @@ Having discussions about your project with other computer vision enthusiasts can
 
 ### Channels to Connect with the Community
 
-- **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
+- **GitHub Issues:** Visit the YOLO11 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Refer to the [official YOLOv8 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Refer to the [official YOLO11 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
 
 ## Your Dataset Is Ready!
 
@@ -151,7 +151,7 @@ Data preprocessing is essential in computer vision projects because it ensures t
 
 ### How can I use Ultralytics YOLO for data augmentation?
 
-For data augmentation with Ultralytics YOLOv8, you need to modify the dataset configuration file (.yaml). In this file, you can specify various augmentation techniques such as random crops, horizontal flips, and brightness adjustments. This can be effectively done using the training configurations [explained here](../modes/train.md). Data augmentation helps create a more robust dataset, reduce [overfitting](https://www.ultralytics.com/glossary/overfitting), and improve model generalization.
+For data augmentation with Ultralytics YOLO11, you need to modify the dataset configuration file (.yaml). In this file, you can specify various augmentation techniques such as random crops, horizontal flips, and brightness adjustments. This can be effectively done using the training configurations [explained here](../modes/train.md). Data augmentation helps create a more robust dataset, reduce [overfitting](https://www.ultralytics.com/glossary/overfitting), and improve model generalization.
 
 ### What are the best data normalization techniques for computer vision data?
 
@@ -160,12 +160,12 @@ Normalization scales pixel values to a standard range for faster convergence and
 - **Min-Max Scaling**: Scales pixel values to a range of 0 to 1.
 - **Z-Score Normalization**: Scales pixel values based on their mean and standard deviation.
 
-For YOLOv8, normalization is handled automatically, including conversion to RGB and pixel value scaling. Learn more about it in the [model training section](../modes/train.md).
+For YOLO11, normalization is handled automatically, including conversion to RGB and pixel value scaling. Learn more about it in the [model training section](../modes/train.md).
 
 ### How should I split my annotated dataset for training?
 
 To split your dataset, a common practice is to divide it into 70% for training, 20% for validation, and 10% for testing. It is important to maintain the data distribution of classes across these splits and avoid data leakage by performing augmentation only on the training set. Use tools like scikit-learn or [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) for efficient dataset splitting. See the detailed guide on [dataset preparation](../guides/data-collection-and-annotation.md).
 
-### Can I handle varying image sizes in YOLOv8 without manual resizing?
+### Can I handle varying image sizes in YOLO11 without manual resizing?
 
-Yes, Ultralytics YOLOv8 can handle varying image sizes through the 'imgsz' parameter during model training. This parameter ensures that images are resized so their largest dimension matches the specified size (e.g., 640 pixels), while maintaining the aspect ratio. For more flexible input handling and automatic adjustments, check the [model training section](../modes/train.md).
+Yes, Ultralytics YOLO11 can handle varying image sizes through the 'imgsz' parameter during model training. This parameter ensures that images are resized so their largest dimension matches the specified size (e.g., 640 pixels), while maintaining the aspect ratio. For more flexible input handling and automatic adjustments, check the [model training section](../modes/train.md).