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

Docs spelling and grammar fixes (#13307)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: RainRat <rainrat78@yahoo.ca>
This commit is contained in:
Glenn Jocher 2024-06-02 14:07:14 +02:00 • committed by GitHub
parent bddea17bf3
commit 064e2fd282
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
48 changed files with 179 additions and 172 deletions
Download patch file Download diff file Expand all files Collapse all files

6
docs/en/integrations/tfjs.md
View file

@ -6,9 +6,9 @@ keywords: Ultralytics YOLOv8, TensorFlow.js, TF.js, Model Deployment, Node.js, M
# Export to TF.js Model Format From a YOLOv8 Model Format
Deploying machine learning models directly in the browser or on Node.js can be tricky. You’ll need to make sure your model format is optimized for faster performance so that the model can be used to run interactive applications locally on the user’s device. The TensorFlow.js, or TF.js, model format is designed to use minimal power while delivering fast performance.
Deploying machine learning models directly in the browser or on Node.js can be tricky. You'll need to make sure your model format is optimized for faster performance so that the model can be used to run interactive applications locally on the user's device. The TensorFlow.js, or TF.js, model format is designed to use minimal power while delivering fast performance.
The ‘export to TF.js model format’ feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and locally-run object detection inference. In this guide, we'll walk you through converting your models to the TF.js format, making it easier for your models to perform well on various local browsers and Node.js applications.
The 'export to TF.js model format' feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and locally-run object detection inference. In this guide, we'll walk you through converting your models to the TF.js format, making it easier for your models to perform well on various local browsers and Node.js applications.
## Why Should You Export to TF.js?
@ -103,7 +103,7 @@ Now that you have exported your YOLOv8 model to the TF.js format, the next step
However, for in-depth instructions on deploying your TF.js models, take a look at the following resources:
- **[Chrome Extension](https://www.tensorflow.org/js/tutorials/deployment/web_ml_in_chrome)**: Here’s the developer documentation for how to deploy your TF.js models to a Chrome extension.
- **[Chrome Extension](https://www.tensorflow.org/js/tutorials/deployment/web_ml_in_chrome)**: Here's the developer documentation for how to deploy your TF.js models to a Chrome extension.
- **[Run TensorFlow.js in Node.js](https://www.tensorflow.org/js/guide/nodejs)**: A TensorFlow blog post on running TensorFlow.js in Node.js directly.