Popular GitHub Repositories for Smartphones

OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation.

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Tensorflow implementation of Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones (ECCV 2018 Workshop PIRM)

Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The re

Transportation Mode Detection with Unconstrained Smartphones Sensors

Agro Doc is basically an app that will help farmers easily pinpoint their crop diseases using their smartphones. The app uses a pre trained tensorflow model to identify issues and then suggest possible cures for the crop infections/diseases. #AndroidDevChallenge

A curated list of awesome tools, frameworks, libraries, and resources for running AI models on edge devices, including smartphones, IoT devices, embedded systems, and hardware accelerators. Edge AI focuses on processing data locally on the device, reducing latency and enhancing privacy.

This repository includes transportation mode detection with unconstrained smartphones sensors using various Machine learning algorithms.

?? ? Contains Matlab codes used for implementation of acceleration signal processing algorithm on smartphones for accident detection. ??

Multiple Connected Residual Network for Image Enhancement on Smartphones (ECCVW 2018)

Mobile AI: iOS CoreML, Android TFLite, on-device inference, ONNX, TensorRT, and ML deployment for smartphones.

The smartphones of the people probably carry the most valueable but also private data. Since using data promisses to be one of the best ways to fight back against COVID-19, it is highly desirable to get access. By using a Federated Learning approach with PySyft it is possible to learn from the private data right on the smartphone, with the data never leaving the device.

Curated collection of on-device AI for iOS and Android: mobile ML frameworks, LLM apps, Core ML, TensorFlow Lite, inference optimization, and privacy-preserving AI that runs entirely on smartphones.

? AI-powered phone recommendation system using Flask, Machine Learning, and Waitress. Classifies user queries and suggests the best smartphones based on their needs.

An intelligent multi-agent system with LLM-powered chatbot, web scraping, and FastAPI backend to query, compare, and review Samsung smartphones.

End to end smartphone price prediction from specs. Web scraped data, leak safe pipelines with target encoding and robust scaling, tuned SVR, and a Streamlit app.

This course provides a hands-on introduction to extreme model quantization, hardware-aware optimization, and on-device deployment for generative AI models. You'll explore advanced techniques to reduce model size, accelerate inference, and deploy compact LLMs on edge devices like Android smartphones.

Low Latency Deep Learning on Smartphones using TensorFlow Lite and CoreML