HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Add Docs glossary links (#16448)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-09-23 23:48:46 +02:00 • committed by GitHub
parent 8b8c25f216
commit 443fbce194
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
193 changed files with 1124 additions and 1124 deletions
Download patch file Download diff file Expand all files Collapse all files

6
docs/en/guides/sahi-tiled-inference.md
View file

@ -6,7 +6,7 @@ keywords: YOLOv8, SAHI, Sliced Inference, Object Detection, Ultralytics, High-re
# Ultralytics Docs: Using YOLOv8 with SAHI for Sliced Inference
Welcome to the Ultralytics documentation on how to use YOLOv8 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLOv8. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLOv8 for enhanced object detection performance.
Welcome to the Ultralytics documentation on how to use YOLOv8 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLOv8. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLOv8 for enhanced [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
<p align="center">
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/sahi-sliced-inference-overview.avif" alt="SAHI Sliced Inference Overview">
@ -31,7 +31,7 @@ SAHI (Slicing Aided Hyper Inference) is an innovative library designed to optimi
- **Seamless Integration**: SAHI integrates effortlessly with YOLO models, meaning you can start slicing and detecting without a lot of code modification.
- **Resource Efficiency**: By breaking down large images into smaller parts, SAHI optimizes the memory usage, allowing you to run high-quality detection on hardware with limited resources.
- **High Accuracy**: SAHI maintains the detection accuracy by employing smart algorithms to merge overlapping detection boxes during the stitching process.
- **High [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: SAHI maintains the detection accuracy by employing smart algorithms to merge overlapping detection boxes during the stitching process.
## What is Sliced Inference?
@ -202,7 +202,7 @@ If you use SAHI in your research or development work, please cite the original S
}
```
We extend our thanks to the SAHI research group for creating and maintaining this invaluable resource for the computer vision community. For more information about SAHI and its creators, visit the [SAHI GitHub repository](https://github.com/obss/sahi).
We extend our thanks to the SAHI research group for creating and maintaining this invaluable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about SAHI and its creators, visit the [SAHI GitHub repository](https://github.com/obss/sahi).
## FAQ