EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]

开源移动端快速视频人脸跟踪-移动端150FPS+

PeleeNet: An efficient DenseNet architecture for mobile devices

Official code (Tensorflow) for paper "Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks"

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.

PeleeNet in PyTorch

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:

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:

An NPM package written in React used to preview mobile devices. Especially helpful when used in combination with a CMS like Sanity.

Android application to participate in experiment crowdsourcing (such as workload crowd-benchmarking and crowd-tuning) using Collective Knowledge Framework and open repositories of knowledge:

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:

A collection of awesome framework, libraries, learning tutorials, videos, webcasts, technical resources and cool stuff about iOS Security.

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.

TransferCL: an open framework for transfer learning on mobile device

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.

Bespoke OTA service for Planet Computers Ltd, supported by cloud-native technology.

🏄 A Lightweight Web Browser-based Machine Learning Framework

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.

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).

Detect device is iOS or Android. 检测是iOS设备还是安卓设备。