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Add https://youtu.be/ZF7EAodHn1U to Docs (#13014)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-05-22 15:20:16 +05:00 • committed by GitHub
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11
docs/en/models/yolov9.md
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@ -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