#大语言模型#Nexa SDK is a comprehensive toolkit for supporting GGML and ONNX models. It supports text generation, image generation, vision-language models (VLM), Audio Language Model, auto-speech-recognition (ASR...
#计算机科学#Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)
This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.
#安卓#A custom RAG pipeline for multi-document QA from PDF/DOCX documents, in Android
This is a web demo for camera-based PPG sensing (rPPG).
#安卓#An Android app running inference on Depth-Anything and Depth-Anything-V2
#自然语言处理#Embeddings from sentence-transformers in Android! Supports all-MiniLM-L6-V2, bge-small-en, snowflake-arctic, model2vec models and more
#计算机科学#Object detection inference with Roboflow Train models on NVIDIA Jetson devices.
#IOS#Approach to implementing distributed training of an ML model: server/device training for iOS.
#计算机科学#[NeurIPS'24] DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators
#计算机科学#Control your computer using hand gestures with AI, using Google's MediaPipe and OAK-D Lite camera.
End-to-end on-device federated learning, "An On-Device Federated Learning System for SMS Spam Classification", IEEE MIT URTC 2022
#学习与技能提升#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 d...
#安卓#An Android app where users draw a number and machine learning does the rest
Instant offline audio transcription using OpenAI's Whisper AI at the power of your fingers!
#安卓#A minimalistic Android app showcasing semantic search using ObjectBox and Lucene KNN, leveraging the MiniLM-L6-V2 embedding model and bert_vocab.txt for efficient retrieval.
*Resource Efficient Federated Deep Learning for IoT Security Monitoring
#安卓#This repository is meant to be a playground for mobile deep learning.
As described in "Towards Full On-Tiny-Device Learning: Guided Search for a Randomly Initialized Neural Network"