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

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

Browse source 
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar 2024-05-22 15:20:16 +05:00 • committed by GitHub
parent 2dc0a62bb1
commit f065931e8e
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 13 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

4
docs/en/models/yolo-world.md
View file

@ -8,8 +8,6 @@ keywords: YOLO-World, YOLOv8, machine learning, CNN-based framework, object dete
The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://ultralytics.com) [YOLOv8](yolov8.md)-based approach for Open-Vocabulary Detection tasks. This innovation enables the detection of any object within an image based on descriptive texts. By significantly lowering computational demands while preserving competitive performance, YOLO-World emerges as a versatile tool for numerous vision-based applications.
![YOLO-World Model architecture overview](https://github.com/ultralytics/ultralytics/assets/26833433/31105058-78c1-43ef-9573-4f41b06df531)
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/cfTKj96TjSE"
@ -21,6 +19,8 @@ The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://ult
<strong>Watch:</strong> YOLO World training workflow on custom dataset
</p>
![YOLO-World Model architecture overview](https://github.com/ultralytics/ultralytics/assets/26833433/31105058-78c1-43ef-9573-4f41b06df531)
## Overview
YOLO-World tackles the challenges faced by traditional Open-Vocabulary detection models, which often rely on cumbersome Transformer models requiring extensive computational resources. These models' dependence on pre-defined object categories also restricts their utility in dynamic scenarios. YOLO-World revitalizes the YOLOv8 framework with open-vocabulary detection capabilities, employing vision-language modeling and pre-training on expansive datasets to excel at identifying a broad array of objects in zero-shot scenarios with unmatched efficiency.