Edge Devices

Deploying ML on Arduino: From Blink to Think

Deploying ML on Arduino Nano 33 BLE. Explore TinyML techniques, setup steps, and why older Arduinos still rival the new Arduino Uno Q.

Deployment, Edge Devices, Image Classification, Machine Learning

Getting Started with VLM on Jetson Nano

Learn how to setup a pipeline to run VLM on Jetson Nano using Huggingface Transformers. Run models like LiquidAI, Moondream2, FastVLM, and SmolVLM.

Edge Devices, Jetson Nano, VLM on Jetson Nano, VLMs

VLM on Edge: Worth the Hype or Just a Novelty?

Testing Vision Language Models (VLM) on edge devices. Check how small VLMs perform on our custom Raspberry Pi Cluster and Jetson Nanos.

Edge Devices, Jetson Nano, Raspberry Pi, VLMs

YOLO11 on Raspberry Pi: Optimizing Object Detection for Edge Devices

Imagine you have multiple warehouses in different places where you don’t have time to monitor everything at a time, and you can’t afford a lot of computes due to their

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