Edge Devices

YOLO11 on Raspberry Pi: Optimizing Object Detection for Edge Devices

Imagine you have multiple warehouses in different places where you don 8217 t have time to monitor everything at a time and you can 8217 t afford a lot of

Computer Vision, Edge Devices, Object Detection, Object Tracking, Raspberry Pi, YOLO

Object Detection on Edge Device: Deploying YOLOv8 on Luxonis OAK-D-Lite – Pothole Datset

This article discusses how to use any finetuned yolov8 pytorch model on oak d lite device with OpenVINO IR Format

Deployment, Edge Devices, Intel OpenVINO Toolkit, OAK

TensorFlow Model Optimization Toolkit – A Deep Dive

In the previous posts of the TFLite series we introduced TFLite and the process of creating a model In this post we will take a deeper dive into the TensorFlow

CNN, Deep Learning, Edge Devices, Image Classification, Model Optimization, Post Training Quantization, Raspberry Pi

TensorFlow Lite Model Maker: Create Models for On-Device Machine Learning

In this article we will learn how to create a TensorFlow Lite model using the TF Lite Model Maker Library We will fine tune a pre trained image classification model

CNN, Deep Learning, Edge Devices, Image Classification, Model Optimization, OpenCV Tutorials, Post Training Quantization, Raspberry Pi

TensorFlow Lite: TFLite Model Optimization for On-Device Machine Learning

The recent trend in developing larger and larger Deep Learning models for a slight increase in accuracy raises concerns about their computational efficiency and wide scaled usability We can not