Add https://youtu.be/q7LwPoM7tSQ to `guides/yolo-performance-metrics.md` (#8114)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

.github/workflows/publish.yml

@@ -33,6 +33,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip wheel build twine
+          pip install "git+https://github.com/squidfunk/mkdocs-material@master"
           pip install -e ".[dev]" --extra-index-url https://download.pytorch.org/whl/cpu
       - name: Check PyPI version
         shell: python

docs/en/guides/distance-calculation.md

@@ -14,7 +14,7 @@ Measuring the gap between two objects is known as distance calculation within a
 
 |                                                  Distance Calculation using Ultralytics YOLOv8                                                  |                                                                
 |:-----------------------------------------------------------------------------------------------------------------------------------------------:|
-| ![Ultralytics YOLOv8 Distance Calculation](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/940fe793-f34c-44e6-8cd7-c2153fde837b) |
+| ![Ultralytics YOLOv8 Distance Calculation](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/96ae9a71-3170-42d8-887c-d903cba74956) |
 
 ## Advantages of Distance Calculation?
 

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

@@ -10,6 +10,17 @@ keywords: YOLOv8, Performance metrics, Object detection, Intersection over Union
 
 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.
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/q7LwPoM7tSQ"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong>  Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, 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.