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

4
docs/en/integrations/tf-savedmodel.md
View file

@ -42,7 +42,7 @@ TF SavedModel provides a range of options to deploy your machine learning models
- **Mobile and Embedded Devices:** TensorFlow Lite, a lightweight solution for running machine learning models on mobile, embedded, and IoT devices, supports converting TF SavedModels to the TensorFlow Lite format. This allows you to deploy your models on a wide range of devices, from smartphones and tablets to microcontrollers and edge devices.
- **TensorFlow Runtime:** TensorFlow Runtime (tfrt) is a high-performance runtime for executing TensorFlow graphs. It provides lower-level APIs for loading and running TF SavedModels in C++ environments. TensorFlow Runtime offers better performance compared to the standard TensorFlow runtime. It is suitable for deployment scenarios that require low-latency inference and tight integration with existing C++ codebases.
- **TensorFlow Runtime:** TensorFlow Runtime (`tfrt`) is a high-performance runtime for executing TensorFlow graphs. It provides lower-level APIs for loading and running TF SavedModels in C++ environments. TensorFlow Runtime offers better performance compared to the standard TensorFlow runtime. It is suitable for deployment scenarios that require low-latency inference and tight integration with existing C++ codebases.
## Exporting YOLOv8 Models to TF SavedModel
@ -105,7 +105,7 @@ Now that you have exported your YOLOv8 model to the TF SavedModel format, the ne
However, for in-depth instructions on deploying your TF SavedModel models, take a look at the following resources:
- **[TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving)**: Here’s the developer documentation for how to deploy your TF SavedModel models using TensorFlow Serving.
- **[TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving)**: Here's the developer documentation for how to deploy your TF SavedModel models using TensorFlow Serving.
- **[Run a TensorFlow SavedModel in Node.js](https://blog.tensorflow.org/2020/01/run-tensorflow-savedmodel-in-nodejs-directly-without-conversion.html)**: A TensorFlow blog post on running a TensorFlow SavedModel in Node.js directly without conversion.