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Add https://youtu.be/tq3FU_QczxE to docs (#13867)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-06-22 00:07:39 +05:00 • committed by GitHub
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docs/en/guides/sahi-tiled-inference.md
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@ -16,6 +16,17 @@ Welcome to the Ultralytics documentation on how to use YOLOv8 with [SAHI](https:
SAHI (Slicing Aided Hyper Inference) is an innovative library designed to optimize object detection algorithms for large-scale and high-resolution imagery. Its core functionality lies in partitioning images into manageable slices, running object detection on each slice, and then stitching the results back together. SAHI is compatible with a range of object detection models, including the YOLO series, thereby offering flexibility while ensuring optimized use of computational resources.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/tq3FU_QczxE"
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> Inference with SAHI (Slicing Aided Hyper Inference) using Ultralytics YOLOv8
</p>
### Key Features of SAHI
- **Seamless Integration**: SAHI integrates effortlessly with YOLO models, meaning you can start slicing and detecting without a lot of code modification.