Update YOLO11 Actions and Docs (#16596)

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

docs/en/guides/index.md

@@ -18,7 +18,7 @@ Whether you're a beginner or an expert in [deep learning](https://www.ultralytic
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Ultralytics YOLOv8 Guides Overview
+  <strong>Watch:</strong> Ultralytics YOLO11 Guides Overview
 </p>
 
 ## Guides
@@ -30,14 +30,14 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Model Deployment Options](model-deployment-options.md): Overview of YOLO [model deployment](https://www.ultralytics.com/glossary/model-deployment) formats like ONNX, OpenVINO, and TensorRT, with pros and cons for each to inform your deployment strategy.
 - [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
 - [Hyperparameter Tuning](hyperparameter-tuning.md) 🚀 NEW: Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms.
-- [SAHI Tiled Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLOv8 for object detection in high-resolution images.
+- [SAHI Tiled Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLO11 for object detection in high-resolution images.
 - [AzureML Quickstart](azureml-quickstart.md) 🚀 NEW: Get up and running with Ultralytics YOLO models on Microsoft's Azure [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml) platform. Learn how to train, deploy, and scale your object detection projects in the cloud.
 - [Conda Quickstart](conda-quickstart.md) 🚀 NEW: Step-by-step guide to setting up a [Conda](https://anaconda.org/conda-forge/ultralytics) environment for Ultralytics. Learn how to install and start using the Ultralytics package efficiently with Conda.
 - [Docker Quickstart](docker-quickstart.md) 🚀 NEW: Complete guide to setting up and using Ultralytics YOLO models with [Docker](https://hub.docker.com/r/ultralytics/ultralytics). Learn how to install Docker, manage GPU support, and run YOLO models in isolated containers for consistent development and deployment.
 - [Raspberry Pi](raspberry-pi.md) 🚀 NEW: Quickstart tutorial to run YOLO models to the latest Raspberry Pi hardware.
 - [NVIDIA Jetson](nvidia-jetson.md) 🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices.
 - [DeepStream on NVIDIA Jetson](deepstream-nvidia-jetson.md) 🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices using DeepStream and TensorRT.
-- [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLOv8 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
+- [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLO11 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
 - [YOLO Thread-Safe Inference](yolo-thread-safe-inference.md) 🚀 NEW: Guidelines for performing inference with YOLO models in a thread-safe manner. Learn the importance of thread safety and best practices to prevent race conditions and ensure consistent predictions.
 - [Isolating Segmentation Objects](isolating-segmentation-objects.md) 🚀 NEW: Step-by-step recipe and explanation on how to extract and/or isolate objects from images using Ultralytics Segmentation.
 - [Edge TPU on Raspberry Pi](coral-edge-tpu-on-raspberry-pi.md): [Google Edge TPU](https://coral.ai/products/accelerator) accelerates YOLO inference on [Raspberry Pi](https://www.raspberrypi.com/).
@@ -46,7 +46,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
 - [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project.
 - [Data Collection and Annotation](data-collection-and-annotation.md) 🚀 NEW: Explore the tools, techniques, and best practices for collecting and annotating data to create high-quality inputs for your computer vision models.
-- [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLOv8, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
+- [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLO11, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
 - [Tips for Model Training](model-training-tips.md) 🚀 NEW: Explore tips on optimizing [batch sizes](https://www.ultralytics.com/glossary/batch-size), using [mixed precision](https://www.ultralytics.com/glossary/mixed-precision), applying pre-trained weights, and more to make training your computer vision model a breeze.
 - [Insights on Model Evaluation and Fine-Tuning](model-evaluation-insights.md) 🚀 NEW: Gain insights into the strategies and best practices for evaluating and fine-tuning your computer vision models. Learn about the iterative process of refining models to achieve optimal results.
 - [A Guide on Model Testing](model-testing.md) 🚀 NEW: A thorough guide on testing your computer vision models in realistic settings. Learn how to verify accuracy, reliability, and performance in line with project goals.
@@ -75,14 +75,14 @@ Training a custom object detection model with Ultralytics YOLO is straightforwar
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8s.pt")  # Load a pre-trained YOLO model
+        model = YOLO("yolo11n.pt")  # Load a pre-trained YOLO model
         model.train(data="path/to/dataset.yaml", epochs=50)  # Train on custom dataset
         ```
 
     === "CLI"
 
         ```bash
-        yolo task=detect mode=train model=yolov8s.pt data=path/to/dataset.yaml epochs=50
+        yolo task=detect mode=train model=yolo11n.pt data=path/to/dataset.yaml epochs=50
         ```
 
 For detailed dataset formatting and additional options, refer to our [Tips for Model Training](model-training-tips.md) guide.