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

Docs Prettier reformat (#13483)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-06-10 12:59:01 +02:00 • committed by GitHub
parent 2f2e81614f
commit e5185ccf63
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
90 changed files with 763 additions and 742 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/en/guides/index.md
View file

@ -44,7 +44,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
- [OpenVINO Latency vs Throughput Modes](optimizing-openvino-latency-vs-throughput-modes.md) - Learn latency and throughput optimization techniques for peak YOLO inference performance.
- [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
- [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.
- [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).
## Contribute to Our Guides