Firebase MLkit vision demo app
Hugging Face Transformers.js wrapper for on-device AI with web-workers
Double Quantization Experiment: Quantizing a Quantized model for On-device Deployment
This course is designed for beginner AI developers, ML engineers, data scientists, and mobile developers looking to deploy optimized models on edge devices
This project implements a complete offline RAG-Fusion application for text generation and retrieval, leveraging efficient local resources.
Python ML library for person fall detection. Intended for IoT deployments with on-device inference and on-device transfer learning.
Custom implementation of Apple Intelligence features
SponsorMe is a project to help provide access to digital tools for learning, powered by on-device machine learning, innovation and willingness, to those people that have limited access to technology due to demographics, disabilities, economy or other multiple reasons.
2022 D.Com 동아리 대항전 Team "AEye" - Android Native App 개발을 위한 Repo.
Kotlin bindings for Edgerunner
iOS Voice Activity Detection (VAD). Supports WebRTC VAD GMM, Silero VAD DNN, Yamnet VAD DNN models.
A python framework for designing high-performance Computer Vision pipelines at the Edge. Supports Coral Edge TPU, Raspberry Pi Camera, and more.
BLOCKSET: Efficient out of core tree ensemble inference
A Google Photos alternative, where your pictures never leave your phone! Classification happens on-device, leveraging pre-trained models. Available for Android and iOS.
Object detection inference with Roboflow Train models on NVIDIA Jetson devices.
Simplified AI runtime integration for mobile app development
Recipes for on-device voice AI and local LLM
Binary Neural Network on IceStick FPGA.
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