mobile-devices
There are 26 repositories under mobile-devices topic.
snap-research/EfficientFormer
EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
zeusees/HyperFT
开源移动端快速视频人脸跟踪-移动端150FPS+
Robert-JunWang/PeleeNet
PeleeNet: An efficient DenseNet architecture for mobile devices
thangvubk/FEQE
Official code (Tensorflow) for paper "Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks"
ctuning/ck-env
CK repository with components and automation actions to enable portable workflows across diverse platforms including Linux, Windows, MacOS and Android. It includes software detection plugins and meta packages (code, data sets, models, scripts, etc) with the possibility of multiple versions to co-exist in a user or system environment.
wpf535236337/pytorch-peleenet
PeleeNet in PyTorch
ctuning/ck-crowdtuning
Collective Knowledge crowd-tuning extension to let users crowdsource their experiments (using portable Collective Knowledge workflows) such as performance benchmarking, auto tuning and machine learning across diverse platforms with Linux, Windows, MacOS and Android provided by volunteers. Demo of DNN crowd-benchmarking and crowd-tuning:
dividiti/crowdsource-video-experiments-on-android
Crowdsourcing video experiments (such as collaborative benchmarking and optimization of DNN algorithms) using Collective Knowledge Framework across diverse Android devices provided by volunteers. Results are continuously aggregated in the open repository:
Jesperpaulsen/sanity-mobile-preview
An NPM package written in React used to preview mobile devices. Especially helpful when used in combination with a CMS like Sanity.
ctuning/crowdsource-experiments-using-android-devices
Android application to participate in experiment crowdsourcing (such as workload crowd-benchmarking and crowd-tuning) using Collective Knowledge Framework and open repositories of knowledge:
ctuning/ck-crowd-scenarios
Public scenarios to crowdsource experiments (such as DNN crowd-benchmarking and crowd-tuning) using Collective Knowledge Framework across diverse mobile devices provided by volunteers. Results are continuously aggregated at the open repository of knowledge:
ctuning/ck-wa
Collective Knowledge workflow for ARM's workload automation tool: an open framework for gathering and sharing knowledge about system design and optimization using real-world workloads.
paulveillard/cybersecurity-iOS
A collection of awesome framework, libraries, learning tutorials, videos, webcasts, technical resources and cool stuff about iOS Security.
OValery16/TransferCL
TransferCL: an open framework for transfer learning on mobile device
nancyalaswad90/Become-IT-Support-Help-Desk
This learning path provides system administrators with a comprehensive method for studying the skills tested in the CompTIA A+ (220-1001 and 220-1002) exams. It includes in-depth courses teaching skills from each exam domain and provides insights into resources you can use to prepare for the exam.
planet-community/planet_ota
Bespoke OTA service for Planet Computers Ltd, supported by cloud-native technology.
Charmve/jsFlow
🏄 A Lightweight Web Browser-based Machine Learning Framework
OsamaM0/FedGreedy-Federated-Learning-System
Federated Learning (FL) is a collaborative machine learning approach that enables decentralized data processing. Instead of collecting and storing data in a central server, FL trains machine learning models directly on devices or servers where the data resides, enhancing privacy and security.
dev-coco/Mobile-Device-Detect
Detect device is iOS or Android. 检测是iOS设备还是安卓设备。
joaocjesus/deviceInfo
Utility for fetching mobile device(s) name(s). It uses DeviceSpecifications and a Google Programmable Search Engine to fetch the information based on provided model code(s).
osodevops/byod-enterprise-security-standard
This standard outlines the security requirements required to protect your organisational data and financial assets
HamzJERDOUJ/MobileShop
A static website of a mobile devices store
hceresetti/development-for-mobile-devices-ii
Development of applications using the React Native framework on mobile operating systems: user interface, microservices and database for automation, testing and debugging purposes.
MikkelAngeles/bsc2020-app
Efficient offline route navigation with multi-criteria objectives on mobile devices
aliabduljabbar/Password-based-ATO-mitigation-using-timing-and-typing-pattern
This project explores user authentication on mobile devices through typing patterns, leveraging touch and motion data. Using machine learning models, particularly LSTM, the research demonstrates superior user classification accuracy compared to traditional RNN models, enhancing security against ATO attacks.