Update YOLO11 Actions and Docs (#16596)

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

docs/en/guides/yolo-performance-metrics.md

@@ -1,14 +1,14 @@
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-description: Explore essential YOLOv8 performance metrics like mAP, IoU, F1 Score, Precision, and Recall. Learn how to calculate and interpret them for model evaluation.
-keywords: YOLOv8 performance metrics, mAP, IoU, F1 Score, Precision, Recall, object detection, Ultralytics
+description: Explore essential YOLO11 performance metrics like mAP, IoU, F1 Score, Precision, and Recall. Learn how to calculate and interpret them for model evaluation.
+keywords: YOLO11 performance metrics, mAP, IoU, F1 Score, Precision, Recall, object detection, Ultralytics
 ---
 
 # Performance Metrics Deep Dive
 
 ## Introduction
 
-Performance metrics are key tools to evaluate the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of [object detection](https://www.ultralytics.com/glossary/object-detection) models. They shed light on how effectively a model can identify and localize objects within images. Additionally, they help in understanding the model's handling of false positives and false negatives. These insights are crucial for evaluating and enhancing the model's performance. In this guide, we will explore various performance metrics associated with YOLOv8, their significance, and how to interpret them.
+Performance metrics are key tools to evaluate the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of [object detection](https://www.ultralytics.com/glossary/object-detection) models. They shed light on how effectively a model can identify and localize objects within images. Additionally, they help in understanding the model's handling of false positives and false negatives. These insights are crucial for evaluating and enhancing the model's performance. In this guide, we will explore various performance metrics associated with YOLO11, their significance, and how to interpret them.
 
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@@ -18,12 +18,12 @@ Performance metrics are key tools to evaluate the [accuracy](https://www.ultraly
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-  <strong>Watch:</strong>  Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, <a href="https://www.ultralytics.com/glossary/precision">Precision</a>, IoU & Accuracy
+  <strong>Watch:</strong>  Ultralytics YOLO11 Performance Metrics | MAP, F1 Score, <a href="https://www.ultralytics.com/glossary/precision">Precision</a>, IoU & Accuracy
 </p>
 
 ## Object Detection Metrics
 
-Let's start by discussing some metrics that are not only important to YOLOv8 but are broadly applicable across different object detection models.
+Let's start by discussing some metrics that are not only important to YOLO11 but are broadly applicable across different object detection models.
 
 - **[Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU):** IoU is a measure that quantifies the overlap between a predicted [bounding box](https://www.ultralytics.com/glossary/bounding-box) and a ground truth bounding box. It plays a fundamental role in evaluating the accuracy of object localization.
 
@@ -35,9 +35,9 @@ Let's start by discussing some metrics that are not only important to YOLOv8 but
 
 - **F1 Score:** The F1 Score is the harmonic mean of precision and recall, providing a balanced assessment of a model's performance while considering both false positives and false negatives.
 
-## How to Calculate Metrics for YOLOv8 Model
+## How to Calculate Metrics for YOLO11 Model
 
-Now, we can explore [YOLOv8's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
+Now, we can explore [YOLO11's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
 
 Using the validation mode is simple. Once you have a trained model, you can invoke the model.val() function. This function will then process the validation dataset and return a variety of performance metrics. But what do these metrics mean? And how should you interpret them?
 
@@ -91,7 +91,7 @@ The model.val() function, apart from producing numeric metrics, also yields visu
 
 - **Validation Batch Labels (`val_batchX_labels.jpg`)**: These images depict the ground truth labels for distinct batches from the validation dataset. They provide a clear picture of what the objects are and their respective locations as per the dataset.
 
-- **Validation Batch Predictions (`val_batchX_pred.jpg`)**: Contrasting the label images, these visuals display the predictions made by the YOLOv8 model for the respective batches. By comparing these to the label images, you can easily assess how well the model detects and classifies objects visually.
+- **Validation Batch Predictions (`val_batchX_pred.jpg`)**: Contrasting the label images, these visuals display the predictions made by the YOLO11 model for the respective batches. By comparing these to the label images, you can easily assess how well the model detects and classifies objects visually.
 
 #### Results Storage
 
@@ -153,56 +153,56 @@ Real-world examples can help clarify how these metrics work in practice.
 
 ## Connect and Collaborate
 
-Tapping into a community of enthusiasts and experts can amplify your journey with YOLOv8. Here are some avenues that can facilitate learning, troubleshooting, and networking.
+Tapping into a community of enthusiasts and experts can amplify your journey with YOLO11. Here are some avenues that can facilitate learning, troubleshooting, and networking.
 
 ### Engage with the Broader Community
 
-- **GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
+- **GitHub Issues:** The YOLO11 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
 
 - **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers.
 
 ### Official Documentation and Resources:
 
-- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+- **Ultralytics YOLO11 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLO11, along with guides on installation, usage, and troubleshooting.
 
-Using these resources will not only guide you through any challenges but also keep you updated with the latest trends and best practices in the YOLOv8 community.
+Using these resources will not only guide you through any challenges but also keep you updated with the latest trends and best practices in the YOLO11 community.
 
 ## Conclusion
 
-In this guide, we've taken a close look at the essential performance metrics for YOLOv8. These metrics are key to understanding how well a model is performing and are vital for anyone aiming to fine-tune their models. They offer the necessary insights for improvements and to make sure the model works effectively in real-life situations.
+In this guide, we've taken a close look at the essential performance metrics for YOLO11. These metrics are key to understanding how well a model is performing and are vital for anyone aiming to fine-tune their models. They offer the necessary insights for improvements and to make sure the model works effectively in real-life situations.
 
-Remember, the YOLOv8 and Ultralytics community is an invaluable asset. Engaging with fellow developers and experts can open doors to insights and solutions not found in standard documentation. As you journey through object detection, keep the spirit of learning alive, experiment with new strategies, and share your findings. By doing so, you contribute to the community's collective wisdom and ensure its growth.
+Remember, the YOLO11 and Ultralytics community is an invaluable asset. Engaging with fellow developers and experts can open doors to insights and solutions not found in standard documentation. As you journey through object detection, keep the spirit of learning alive, experiment with new strategies, and share your findings. By doing so, you contribute to the community's collective wisdom and ensure its growth.
 
 Happy object detecting!
 
 ## FAQ
 
-### What is the significance of [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) in evaluating YOLOv8 model performance?
+### What is the significance of [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) in evaluating YOLO11 model performance?
 
-Mean Average Precision (mAP) is crucial for evaluating YOLOv8 models as it provides a single metric encapsulating precision and recall across multiple classes. mAP@0.50 measures precision at an IoU threshold of 0.50, focusing on the model's ability to detect objects correctly. mAP@0.50:0.95 averages precision across a range of IoU thresholds, offering a comprehensive assessment of detection performance. High mAP scores indicate that the model effectively balances precision and recall, essential for applications like autonomous driving and surveillance.
+Mean Average Precision (mAP) is crucial for evaluating YOLO11 models as it provides a single metric encapsulating precision and recall across multiple classes. mAP@0.50 measures precision at an IoU threshold of 0.50, focusing on the model's ability to detect objects correctly. mAP@0.50:0.95 averages precision across a range of IoU thresholds, offering a comprehensive assessment of detection performance. High mAP scores indicate that the model effectively balances precision and recall, essential for applications like autonomous driving and surveillance.
 
-### How do I interpret the Intersection over Union (IoU) value for YOLOv8 object detection?
+### How do I interpret the Intersection over Union (IoU) value for YOLO11 object detection?
 
 Intersection over Union (IoU) measures the overlap between the predicted and ground truth bounding boxes. IoU values range from 0 to 1, where higher values indicate better localization accuracy. An IoU of 1.0 means perfect alignment. Typically, an IoU threshold of 0.50 is used to define true positives in metrics like mAP. Lower IoU values suggest that the model struggles with precise object localization, which can be improved by refining bounding box regression or increasing annotation accuracy.
 
-### Why is the F1 Score important for evaluating YOLOv8 models in object detection?
+### Why is the F1 Score important for evaluating YOLO11 models in object detection?
 
-The F1 Score is important for evaluating YOLOv8 models because it provides a harmonic mean of precision and recall, balancing both false positives and false negatives. It is particularly valuable when dealing with imbalanced datasets or applications where either precision or recall alone is insufficient. A high F1 Score indicates that the model effectively detects objects while minimizing both missed detections and false alarms, making it suitable for critical applications like security systems and medical imaging.
+The F1 Score is important for evaluating YOLO11 models because it provides a harmonic mean of precision and recall, balancing both false positives and false negatives. It is particularly valuable when dealing with imbalanced datasets or applications where either precision or recall alone is insufficient. A high F1 Score indicates that the model effectively detects objects while minimizing both missed detections and false alarms, making it suitable for critical applications like security systems and medical imaging.
 
-### What are the key advantages of using Ultralytics YOLOv8 for real-time object detection?
+### What are the key advantages of using Ultralytics YOLO11 for real-time object detection?
 
-Ultralytics YOLOv8 offers multiple advantages for real-time object detection:
+Ultralytics YOLO11 offers multiple advantages for real-time object detection:
 
 - **Speed and Efficiency**: Optimized for high-speed inference, suitable for applications requiring low latency.
 - **High Accuracy**: Advanced algorithm ensures high mAP and IoU scores, balancing precision and recall.
 - **Flexibility**: Supports various tasks including object detection, segmentation, and classification.
 - **Ease of Use**: User-friendly interfaces, extensive documentation, and seamless integration with platforms like Ultralytics HUB ([HUB Quickstart](../hub/quickstart.md)).
 
-This makes YOLOv8 ideal for diverse applications from autonomous vehicles to smart city solutions.
+This makes YOLO11 ideal for diverse applications from autonomous vehicles to smart city solutions.
 
-### How can validation metrics from YOLOv8 help improve model performance?
+### How can validation metrics from YOLO11 help improve model performance?
 
-Validation metrics from YOLOv8 like precision, recall, mAP, and IoU help diagnose and improve model performance by providing insights into different aspects of detection:
+Validation metrics from YOLO11 like precision, recall, mAP, and IoU help diagnose and improve model performance by providing insights into different aspects of detection:
 
 - **Precision**: Helps identify and minimize false positives.
 - **Recall**: Ensures all relevant objects are detected.