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TUTORIAL

YOLO11: Speed Boosted by 7x with OpenVINO on LattePanda MU

By DFRobot

In fast-paced computer vision, models advance to boost accuracy, speed, or both. YOLO11 (from YOLOv8) improves, benchmarked on LattePanda MU x86 SBC for detection, segmentation, pose estimation. OpenVINO shows its efficiency/speed, suitable for this device.

TUTORIAL

Run SLMs (phi3, gemma2, mathstral, llama3.1) on Lattepanda Mu

By DFRobot

In the era of intelligent computing, popular SBCs and key SLMs are analyzed here. We examine SLMs (mathstral, phi 3, etc.) on Lattepanda Mu x86 (Ubuntu 22.04), comparing speed, size, licenses, frameworks to aid developers.

PROJECT

Run YOLOv8 on LattePanda Mu (Intel N100 Processor) with OpenVINO

By DFRobot

This article introduces the use of DFRobot's latest micro x86 computing module,LattePanda Mu,to run YOLOv8 with acceleration by OpenVINO,achieving efficient and accurate object detection while addressing the issues of large size and inconvenience associated with traditional high-performance computer

PROJECT
142

A Hacker's Dream Machine: Cyberdeck with a LattePanda Mu

By Chloe.ou

Discover a stunning custom-built cyberdeck powered by the LattePanda Mu, featuring a slide-out mechanical keyboard, dual-boot Linux Mint and Windows 11, and a CNC-machined aluminum case. Designed by a U.S.-based web designer, this futuristic project showcases the power of modular computing and creative hardware design.

PROJECT
319

Maximizing Home Automation Capabilities with LattePanda Mu

By Chloe.ou

This article introduces an AI-driven smart home project developed by Intel engineer Oliver Hamilton using the ​LattePanda Mu development board. By integrating ​computer vision and ​edge computing technologies, the system automates home lighting control through human pose recognition. The project leverages ​Intel Geti for model training, ​OpenVINO for low-power inference on the ARM-based LattePanda Mu, and ​MQTT combined with ​Node-RED to orchestrate smart lighting (Lifx/Hue) based on user status (e.g., "at desk" or "away"). The solution addresses key smart home challenges by minimizing manual intervention and optimizing energy consumption, demonstrating the potential of compact, high-performance hardware in creating responsive AI-driven living spaces