There are 2 repositories under on-device-ai topic.
[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"
TinyChatEngine: On-Device LLM Inference Library
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
A ready-to-use, minimal app that converts any speech into text.
Android Voice Activity Detection (VAD) library. Supports WebRTC VAD GMM, Silero VAD DNN, Yamnet VAD DNN models.
AubAI brings you on-device gen-AI capabilities, including offline text generation and more, directly within your app.
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)
[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Source code for my blog post series "On-device training with Core ML"
This is a web demo for camera-based PPG sensing (rPPG).
Recipes for on-device voice AI and local LLM
Simplified AI runtime integration for mobile app development
Binary Neural Network on IceStick FPGA.
Kotlin bindings for Edgerunner
Object detection inference with Roboflow Train models on NVIDIA Jetson devices.
A Google Photos alternative, where your pictures never leave your phone! Classification happens on-device, leveraging pre-trained models. Available for Android and iOS.
2022 D.Com 동아리 대항전 Team "AEye" - Android Native App 개발을 위한 Repo.
A python framework for designing high-performance Computer Vision pipelines at the Edge. Supports Coral Edge TPU, Raspberry Pi Camera, and more.
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.
Python ML library for person fall detection. Intended for IoT deployments with on-device inference and on-device transfer learning.
Firebase MLkit vision demo app
Double Quantization Experiment: Quantizing a Quantized model for On-device Deployment