Securade.ai HUB - A generative AI based edge platform for computer vision that connects to existing CCTV cameras and makes them smart.

Signed MQTT deployments for edge fleets: control plane + agent, mTLS, Ed25519 command verification, Docker/K8s.

A lightweight, edge-first web framework built on Cloudflare Workers with authentication, D1 database, and a clean dark UI. Deploy a blog or small site globally in under 5 minutes. Version 1.0

Ingredient to Sugar Level Estimation (from training in Python to edge deployment in JS/TS)

Real-time SAM2 segmentation on edge devices - 40x faster C++ inference with ONNX Runtime for iOS/Android deployment

Proyek ini menggunakan kerangka deteksi objek berbasis YOLO (You Only Look Once) untuk memantau ternak ayam secara real-time lewat kamera CCTV IP. Sistem kemudian mengintegrasikan hasil deteksi dengan komponen IoT (seperti kamera, pengiriman data via MQTT/HTTP, dan perangkat edge)

This project is an AI-powered mobile application capable of recognizing age, gender, and facial expressions from images.

Light-weight 6D pose estimation for Edge devices

Example app using React Create App & Digital Optimization Group's ADN & CMS

Production-ready responsive web interface built with semantic HTML5, CSS3, and vanilla JavaScript. Focused on layout fluidity, static rendering performance, and edge delivery optimization.

This repository contains the complete pipeline for an edge-deployable computer vision model designed to analyze images and detect insulator defects. The model is trained to be lightweight and optimized, ensuring it runs efficiently on edge devices like the Nvidia Jetson Nano.

YOLOv3-YOLO12 unified pipeline for edge deployment - Detection, segmentation, pose estimation with PyTorch to ONNX/TFLite/CoreML export

edge AI model deployment toolkit for Google Coral TPU — Part 3 of 3 in the Philippine license plate recognition pipeline

Industrial computer vision workflow for welding defect inspection using YOLO, OpenCV preprocessing, dataset QA, threshold governance, and edge-readiness analysis.

ASSTF (Adaptive State-Space Transfer Function): A PyTorch framework for dynamic neural topology that reduces parameters by 5-10x, enables test-time adaptation, and outperforms static models on structure-sensitive tasks.

Task-adaptive pruning framework for deploying Vision Transformers on heterogeneous edge devices without accessing private data (arXiv 2601.02437)

Toolset for creating and publishing OS images with automated TPM attestation process for Azure IoT Edge.

Optimized CNN achieving ~89% accuracy with 38.6% parameter reduction for production-ready digit recognition

Code of the paper "Emotion Recognition on Edge Devices: Training and Deployment " by Pandelea et al.

UVA DS 6050 final project. This aims to build smaller models that are easier to use on edge devices