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

Add Tips for Model Training Docs Page (#14011)

Browse source 
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Abirami Vina 2024-06-26 20:04:11 +05:30 • committed by GitHub
parent 08acdd198a
commit 5332f52f1e
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
4 changed files with 182 additions and 7 deletions
Download patch file Download diff file Expand all files Collapse all files

1
docs/en/guides/index.md
View file

@ -46,6 +46,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
- [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project.
- [Data Collection and Annotation](data-collection-and-annotation.md) 🚀 NEW: Explore the tools, techniques, and best practices for collecting and annotating data to create high-quality inputs for your computer vision models.
- [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLOv8, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
- [Tips for Model Training](model-training-tips.md) 🚀 NEW: Explore tips on optimizing batch sizes, using mixed precision, applying pre-trained weights, and more to make training your computer vision model a breeze.
- [ROS Quickstart](ros-quickstart.md) 🚀 NEW: Learn how to integrate YOLO with the Robot Operating System (ROS) for real-time object detection in robotics applications, including Point Cloud and Depth images.
## Contribute to Our Guides