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

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

Object Detection With Depth Measurement Using Pre-trained Models With OAK-D

This is the third blog post in the Oak series If you haven 8217 t checked out the previous posts on OAK check them below In this post we are

3D Computer Vision, CNN, Edge Devices, Face Detection, OAK