Fix mkdocs.yml raw image URLs (#14213)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

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

@@ -174,3 +174,39 @@ In this guide, we've taken a close look at the essential performance metrics for
 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.
 
 Happy object detecting!
+
+## FAQ
+
+### What is the significance of Mean Average Precision (mAP) in evaluating YOLOv8 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.
+
+### How do I interpret the Intersection over Union (IoU) value for YOLOv8 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?
+
+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.
+
+### What are the key advantages of using Ultralytics YOLOv8 for real-time object detection?
+
+Ultralytics YOLOv8 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.
+
+### How can validation metrics from YOLOv8 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:
+
+- **Precision**: Helps identify and minimize false positives.
+- **Recall**: Ensures all relevant objects are detected.
+- **mAP**: Offers an overall performance snapshot, guiding general improvements.
+- **IoU**: Helps fine-tune object localization accuracy.
+
+By analyzing these metrics, specific weaknesses can be targeted, such as adjusting confidence thresholds to improve precision or gathering more diverse data to enhance recall. For detailed explanations of these metrics and how to interpret them, check [Object Detection Metrics](#object-detection-metrics).