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Add https://youtu.be/SMSJvjUG1ko to yolo12.md (#19358)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2025-02-21 16:02:13 +05:00 • committed by GitHub
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docs/en/models/yolo12.md
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@ -10,7 +10,16 @@ keywords: YOLO12, attention-centric object detection, YOLO series, Ultralytics,
YOLO12 introduces an attention-centric architecture that departs from the traditional CNN-based approaches used in previous YOLO models, yet retains the real-time inference speed essential for many applications. This model achieves state-of-the-art object detection accuracy through novel methodological innovations in attention mechanisms and overall network architecture, while maintaining real-time performance.
![YOLO12 comparison visualization](https://github.com/user-attachments/assets/8009d90f-b43c-4a96-bb89-47ef843e7144)
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/mcqTxD-FD5M"
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 YOLO12 for Object Detection with the Ultralytics Package | Is YOLO12 Fast or Slow? 🚀
</p>
## Key Features
@ -29,6 +38,8 @@ YOLO12 introduces an attention-centric architecture that departs from the tradit
- **Enhanced Efficiency**: Achieves higher accuracy with fewer parameters compared to many prior models, demonstrating an improved balance between speed and accuracy.
- **Flexible Deployment**: Designed for deployment across diverse platforms, from edge devices to cloud infrastructure.
![YOLO12 comparison visualization](https://github.com/user-attachments/assets/8009d90f-b43c-4a96-bb89-47ef843e7144)
## Supported Tasks and Modes
YOLO12 supports a variety of computer vision tasks. The table below shows task support and the operational modes (Inference, Validation, Training, and Export) enabled for each: