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Ondevice AI: How to Improve Latency and Accuracy of Neu

作者:禅与计算机程序设计艺术

1.简介

On-device AI (ODA) refers to artificial intelligence technologies that are implemented within the device itself rather than using a cloud computing platform or a dedicated machine learning cluster for training and inference purposes. One key benefit of ODA is its reduced latency and energy consumption compared with traditional cloud solutions. However, despite its potential benefits, implementing an effective ODA solution can be challenging as it requires expertise in computer vision, machine learning, embedded systems development, mobile application development, and networking. In this article, we will discuss how to build an efficient and accurate object detection model directly on smartphones using only open source libraries and tools.

In this tutorial, we will use Google's TensorFlow Lite framework to create a simple object detection model which can detect different types of objects such as persons, cars, bicycle


本文转载自: https://blog.csdn.net/universsky2015/article/details/132364162
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