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

Optimize Docs images (#15900)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar 2024-08-30 05:52:10 +05:00 • committed by GitHub
parent 0f9f7b806c
commit cfebb5f26b
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
174 changed files with 537 additions and 537 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/en/integrations/ray-tune.md
View file

@ -15,7 +15,7 @@ Hyperparameter tuning is vital in achieving peak model performance by discoverin
### Ray Tune
<p align="center">
<img width="640" src="https://docs.ray.io/en/latest/_images/tune_overview.png" alt="Ray Tune Overview">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/ray-tune-overview.avif" alt="Ray Tune Overview">
</p>
[Ray Tune](https://docs.ray.io/en/latest/tune/index.html) is a hyperparameter tuning library designed for efficiency and flexibility. It supports various search strategies, parallelism, and early stopping strategies, and seamlessly integrates with popular machine learning frameworks, including Ultralytics YOLOv8.