Add https://youtu.be/5XYdm5CYODA to docs (#16392)

docs/en/tasks/obb.md

@@ -19,9 +19,8 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
 
     YOLOv8 OBB models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
 
-<table>
-  <tr>
-    <td align="center">
+<p align="center">
+  <br>
   <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Z7Z9pHF8wJc"
     title="YouTube video player" frameborder="0"
     allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
@@ -29,18 +28,7 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
   </iframe>
   <br>
   <strong>Watch:</strong> Object Detection using Ultralytics YOLOv8 Oriented Bounding Boxes (YOLOv8-OBB)
-    </td>
-    <td align="center">
-      <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uZ7SymQfqKI"
-        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> Object Detection with YOLOv8-OBB using Ultralytics HUB
-    </td>
-  </tr>
-</table>
+</p>
 
 ## Visual Samples
 
@@ -98,6 +86,17 @@ Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 epochs at image size 640.
         yolo obb train data=dota8.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
         ```
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uZ7SymQfqKI"
+    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> How to Train Ultralytics YOLOv8-OBB (Oriented Bounding Boxes) Models on DOTA Dataset using Ultralytics HUB
+</p>
+
 ### Dataset format
 
 OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md).
@@ -158,6 +157,17 @@ Use a trained YOLOv8n-obb model to run predictions on images.
         yolo obb predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'  # predict with custom model
         ```
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5XYdm5CYODA"
+    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> How to Detect and Track Storage Tanks using Ultralytics YOLOv8-OBB | Oriented Bounding Boxes | DOTA
+</p>
+
 See full `predict` mode details in the [Predict](../modes/predict.md) page.
 
 ## Export