Add https://youtu.be/eHuzCNZeu0g to docs and integration updates (ult…

…ralytics#9525)
hmurari · Apr 17, 2024 · e9e8a47 · e9e8a47
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diff --git a/docs/en/datasets/segment/carparts-seg.md b/docs/en/datasets/segment/carparts-seg.md
@@ -10,6 +10,17 @@ The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dat
 
 Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects.
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/eHuzCNZeu0g"
+    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> Carparts Instance Segmentation Using Ultralytics HUB
+</p>
+
 ## Dataset Structure
 
 The data distribution within the Carparts Segmentation Dataset is organized as outlined below:

diff --git a/docs/en/integrations/tf-graphdef.md b/docs/en/integrations/tf-graphdef.md
@@ -10,6 +10,10 @@ When you are deploying cutting-edge computer vision models, like YOLOv8, in diff
 
 In this guide, we'll walk you step by step through how to export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to the TF GraphDef model format. By converting your model, you can streamline deployment and use YOLOv8's computer vision capabilities in a broader range of applications and platforms.
 
+<p align="center">
+  <img width="640" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/2d793b51-19f2-49e0-bf4b-5208f2eb5993" alt="TensorFlow GraphDef">
+</p>
+
 ## Why Should You Export to TF GraphDef?
 
 TF GraphDef is a powerful component of the TensorFlow ecosystem that was developed by Google. It can be used to optimize and deploy models like YOLOv8. Exporting to TF GraphDef lets us move models from research to real-world applications. It allows models to run in environments without the full TensorFlow framework.