diff --git a/docs/en/models/yolo-world.md b/docs/en/models/yolo-world.md
index 3052de5e..33517b47 100644
--- a/docs/en/models/yolo-world.md
+++ b/docs/en/models/yolo-world.md
@@ -8,8 +8,6 @@ keywords: YOLO-World, YOLOv8, machine learning, CNN-based framework, object dete
 
 The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://ultralytics.com) [YOLOv8](yolov8.md)-based approach for Open-Vocabulary Detection tasks. This innovation enables the detection of any object within an image based on descriptive texts. By significantly lowering computational demands while preserving competitive performance, YOLO-World emerges as a versatile tool for numerous vision-based applications.
 
-![YOLO-World Model architecture overview](https://github.com/ultralytics/ultralytics/assets/26833433/31105058-78c1-43ef-9573-4f41b06df531)
-
 <p align="center">
   <br>
   <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/cfTKj96TjSE"
@@ -21,6 +19,8 @@ The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://ult
   <strong>Watch:</strong> YOLO World training workflow on custom dataset
 </p>
 
+![YOLO-World Model architecture overview](https://github.com/ultralytics/ultralytics/assets/26833433/31105058-78c1-43ef-9573-4f41b06df531)
+
 ## Overview
 
 YOLO-World tackles the challenges faced by traditional Open-Vocabulary detection models, which often rely on cumbersome Transformer models requiring extensive computational resources. These models' dependence on pre-defined object categories also restricts their utility in dynamic scenarios. YOLO-World revitalizes the YOLOv8 framework with open-vocabulary detection capabilities, employing vision-language modeling and pre-training on expansive datasets to excel at identifying a broad array of objects in zero-shot scenarios with unmatched efficiency.
diff --git a/docs/en/models/yolov9.md b/docs/en/models/yolov9.md
index e89161dd..f480afd4 100644
--- a/docs/en/models/yolov9.md
+++ b/docs/en/models/yolov9.md
@@ -8,6 +8,17 @@ keywords: YOLOv9, real-time object detection, Programmable Gradient Information,
 
 YOLOv9 marks a significant advancement in real-time object detection, introducing groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). This model demonstrates remarkable improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the MS COCO dataset. The YOLOv9 project, while developed by a separate open-source team, builds upon the robust codebase provided by [Ultralytics](https://ultralytics.com) [YOLOv5](yolov5.md), showcasing the collaborative spirit of the AI research community.
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ZF7EAodHn1U"
+    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> YOLOv9 Training on Custom Data using Ultralytics | Industrial Package Dataset
+</p>
+
 ![YOLOv9 performance comparison](https://github.com/ultralytics/ultralytics/assets/26833433/9f41ef7b-6008-43eb-8ba1-0a9b89600100)
 
 ## Introduction to YOLOv9