Scaleout Edge: Sovereign Edge AI orchestration and Federated Learning
In this repository, we explore model compression for transformer architectures via quantization. We specifically explore quantization aware training of the linear layers and demonstrate the performance for 8 bits, 4 bits, 2 bits and 1 bit (binary) quantization.
A camera for measuring sediment grain sizes with edge ML
An awesome list of "small but mighty" models and resources.
BEAVER automates the Edge AI lifecycle through LLM-powered orchestration. It Builds, Evolves, Analyzes, Validates, Executes, and Repairs—just like beavers in nature, but for edge devices!
ESP32-S3 WiFi CSI Human Activity Recognition system with real-time RF sensing, ML inference, and live visualization dashboard.
Notes and resources from Qualcomm On-device AI course, provided by DeepLearningAI
embedded software components for event-based application development
Python ML library for person fall detection. Intended for IoT deployments with on-device inference and on-device transfer learning.
Edge-first XRF V2 benchmark for deployable wearable event detection (earbuds + smart-glasses), with FP/hour-calibrated metrics and reproducible run artifacts.
Fault-tolerant Edge ML pipeline for space weather (TEC) prediction. Fuses raw ISRO telemetry with NASA APIs for sub-second, on-device inference via quantized TensorFlow Lite.
Lightweight Attention U-Net for Breast Cancer Semantic Segmentation
A curated list of machine learning models that run locally on edge devices, including phones, browsers, laptops, Raspberry Pi-class boards, Jetson, Coral, NPUs, and microcontrollers.
Pre-quantized models for edge inference on Cortex-M, ESP32-S3, and Raspberry Pi. Six models — keyword spotting, person detection, hand-gesture recognition, anomaly detection, voice activity detection, and binary defect classifier — each shipped as TF
A system for monitoring statistical data distribution shifts in distributed settings
A Chrome Extension that integrates Machine Learning directly in the browser using TensorFlow.js to classify Gmail emails as important or not important, automatically highlighting important ones with a subtle red tint.
A lightweight, resource-efficient MLOps monitoring solution for machine learning models deployed on edge devices. Features system health tracking, model I/O logging, drift detection, and cloud telemetry.
Fully local semantic search for your images, gifs and other visual assets
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