Learn how to deploy complex machine learning models on single-board computers, mobile phones, and microcontrollers. Main Features ● Comprehensive understanding of the core concepts of TinyML. ● Learn how to design your own TinyML applications from scratch. ● Explore cutting-edge models, hardware, and software platforms for developing TinyML. Description TinyML is an innovative technology that … Read more
1. Introduction Weakly Supervised Object Localization is used to discover the location of target objects within images. Traditional object detection methods typically require precise bounding box annotations for each training sample, which can be time-consuming and labor-intensive for large-scale datasets. To address this issue, weakly supervised object localization solves the problem by using simpler annotation … Read more
1. Introduction Voice recognition, as an important method of human-computer interaction, is gradually becoming one of the core functions of smart devices. However, traditional voice recognition systems often rely on cloud servers for audio data processing and analysis, which brings issues such as latency and privacy. TensorFlow Lite provides an efficient and fast solution for … Read more
Click the blue text to follow us Introduction In today’s Internet of Things era, the application of voice recognition technology is becoming increasingly widespread. This article will introduce how to deploy a TinyML voice recognition system and detail the process from data collection to model deployment. Getting Started with TinyML: Implementing TinyML Voice Recognition Using … Read more
According to reports from Electronic Enthusiasts (Written by Li Wanwan), in the era of artificial intelligence, more and more AI applications need to extend from the cloud to the edge, such as smart headphones, smart cameras, smart bracelets, logistics robots, etc. Deploying AI at the edge has become a trend. With the rapid development of … Read more
Introduction This page will guide you on installing TensorFlow on a Raspberry Pi 4 running the 64-bit Bullseye operating system. TensorFlow is a large software library developed specifically for deep learning, which consumes a lot of resources. You can run TensorFlow on a Raspberry Pi 4, but do not expect miraculous performance. If the model … Read more
Author丨Zhang Ke Address丨https://blog.csdn.net/u013872857/article/details/109045686 Android Studio 4.1 has been released, fixing a total of 2370 bugs and 275 issues. The main new features include: Design Material Design component library updates Development Database Inspector feature Run the emulator directly in Android Studio Dagger navigation support Use TensorFlow Lite models Build and Testing Android emulator supports foldable screens … Read more
Reprint please specify the source: http://www.codelast.com/ Software and hardware environment of this article: Raspberry Pi: Model B V1.2, 1GB RAM OS: Raspbian Jessie Deep Learning is very popular now, and Raspberry Pi enthusiasts certainly won’t miss out. There are already many Raspberry Pi projects that have jumped on the Deep Learning bandwagon. Purely for fun, … Read more
The Editor Says: Currently, researchers are using various deep learning frameworks. This article introduces six common deep learning frameworks and discusses the advantages of PyTorch compared to them. This article is excerpted from “Deep Learning Framework PyTorch: Introduction and Practice”. For more details, please click Read the Original. The Birth of PyTorch In January 2017, … Read more
These Raspberry Pi projects are designed to simplify your life and enhance productivity. — Lauren Pritchett Nothing is more exciting than experiencing the results of your Raspberry Pi creations. After hours of programming, testing, and building from scratch, your project finally takes shape, and you can’t help but exclaim, “Wow!” The possibilities that Raspberry Pi … Read more