Add Docs glossary links (#16448)

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

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

@@ -8,7 +8,7 @@ keywords: YOLOv8 performance metrics, mAP, IoU, F1 Score, Precision, Recall, obj
 
 ## Introduction
 
-Performance metrics are key tools to evaluate the accuracy and efficiency of 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 YOLOv8, their significance, and how to interpret them.
 
 <p align="center">
   <br>
@@ -18,14 +18,14 @@ Performance metrics are key tools to evaluate the accuracy and efficiency of obj
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong>  Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, Precision, IoU & Accuracy
+  <strong>Watch:</strong>  Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, [Precision](https://www.ultralytics.com/glossary/precision), 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.
 
-- **Intersection over Union (IoU):** IoU is a measure that quantifies the overlap between a predicted bounding box and a ground truth bounding box. It plays a fundamental role in evaluating the accuracy of object localization.
+- **[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.
 
 - **Average Precision (AP):** AP computes the area under the precision-recall curve, providing a single value that encapsulates the model's precision and recall performance.
 
@@ -77,15 +77,15 @@ For users validating on the COCO dataset, additional metrics are calculated usin
 
 The model.val() function, apart from producing numeric metrics, also yields visual outputs that can provide a more intuitive understanding of the model's performance. Here's a breakdown of the visual outputs you can expect:
 
-- **F1 Score Curve (`F1_curve.png`)**: This curve represents the F1 score across various thresholds. Interpreting this curve can offer insights into the model's balance between false positives and false negatives over different thresholds.
+- **F1 Score Curve (`F1_curve.png`)**: This curve represents the [F1 score](https://www.ultralytics.com/glossary/f1-score) across various thresholds. Interpreting this curve can offer insights into the model's balance between false positives and false negatives over different thresholds.
 
-- **Precision-Recall Curve (`PR_curve.png`)**: An integral visualization for any classification problem, this curve showcases the trade-offs between precision and recall at varied thresholds. It becomes especially significant when dealing with imbalanced classes.
+- **Precision-Recall Curve (`PR_curve.png`)**: An integral visualization for any classification problem, this curve showcases the trade-offs between precision and [recall](https://www.ultralytics.com/glossary/recall) at varied thresholds. It becomes especially significant when dealing with imbalanced classes.
 
 - **Precision Curve (`P_curve.png`)**: A graphical representation of precision values at different thresholds. This curve helps in understanding how precision varies as the threshold changes.
 
 - **Recall Curve (`R_curve.png`)**: Correspondingly, this graph illustrates how the recall values change across different thresholds.
 
-- **Confusion Matrix (`confusion_matrix.png`)**: The confusion matrix provides a detailed view of the outcomes, showcasing the counts of true positives, true negatives, false positives, and false negatives for each class.
+- **[Confusion Matrix](https://www.ultralytics.com/glossary/confusion-matrix) (`confusion_matrix.png`)**: The confusion matrix provides a detailed view of the outcomes, showcasing the counts of true positives, true negatives, false positives, and false negatives for each class.
 
 - **Normalized Confusion Matrix (`confusion_matrix_normalized.png`)**: This visualization is a normalized version of the confusion matrix. It represents the data in proportions rather than raw counts. This format makes it simpler to compare the performance across classes.
 
@@ -123,7 +123,7 @@ It's important to understand the metrics. Here's what some of the commonly obser
 
 - **Low Precision:** The model may be detecting too many non-existent objects. Adjusting confidence thresholds might reduce this.
 
-- **Low Recall:** The model could be missing real objects. Improving feature extraction or using more data might help.
+- **Low Recall:** The model could be missing real objects. Improving [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) or using more data might help.
 
 - **Imbalanced F1 Score:** There's a disparity between precision and recall.
 
@@ -177,7 +177,7 @@ Happy object detecting!
 
 ## FAQ
 
-### What is the significance of Mean Average Precision (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 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.