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Add https://youtu.be/HMOoM2NwFIQ to docs (#20020)

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Signed-off-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
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docs/en/models/yoloe.md
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@ -12,6 +12,17 @@ keywords: YOLOE, open-vocabulary detection, real-time object detection, instance
[YOLOE (Real-Time Seeing Anything)](https://arxiv.org/html/2503.07465v1) is a new advancement in zero-shot, promptable YOLO models, designed for **open-vocabulary** detection and segmentation. Unlike previous YOLO models limited to fixed categories, YOLOE uses text, image, or internal vocabulary prompts, enabling real-time detection of any object class. Built upon YOLOv10 and inspired by [YOLO-World](yolo-world.md), YOLOE achieves **state-of-the-art zero-shot performance** with minimal impact on speed and accuracy.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/HMOoM2NwFIQ"
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 use YOLOE with Ultralytics Python package: Open Vocabulary & Real-Time Seeing Anything 🚀
</p>
Compared to earlier YOLO models, YOLOE significantly boosts efficiency and accuracy. It improves by **+3.5 AP** over YOLO-Worldv2 on LVIS while using just a third of the training resources and achieving 1.4× faster inference speeds. Fine-tuned on COCO, YOLOE-v8-large surpasses YOLOv8-L by **0.1 mAP**, using nearly **4× less training time**. This demonstrates YOLOE's exceptional balance of accuracy, efficiency, and versatility. The sections below explore YOLOE's architecture, benchmark comparisons, and integration with the [Ultralytics](https://www.ultralytics.com/) framework.
## Architecture Overview