TensorFlow Lite example with Xamarin

A simple app that classifies dog breeds with the use of Tensorflow lite.

TensorFlow binding library for VA Smalltalk

2020-2 컴퓨터비전 Project list

Iranian credit card reader

Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks

Parkinson's Detection App using Machine Learning written in Kotlin.

An Android app that can create pastiches!🎨

Handwrite the Korean alphabet, Hangul, like a native.

An explorer bot for Instagram, which crawls profile pages through their followers/following list and analyses their profile photo and optionally few posts from the top for a specific person's face, using the Tensorflow Lite Java library!

TFLite와 카메라를 이용한 실시간 이미지 분류기 앱

Targmly is an android application for translating languages from one to another, but what is unique about this application is that it also translates English sign language into any other language

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

Leaf buddy's server for classifying given image from the client (Final Project - Mobile Programming)

An app to identify dogs using Tensorflow Lite machine learning models.

The "AI Landmark Recognition Android" repository contains a demonstration project implementing landmark recognition using artificial intelligence on Android devices. By leveraging TensorFlow Lite and CameraX, the application accurately identifies prominent landmarks in real-time. This repository includes the source code and pre-trained models

RoboBoat Competition

"IoT Made Easy!" - This application shows how to use TensorFlow Lite to build a basic machine learning application on a PIC32CX-BZ2 / WBZ451 device. The application classifies the orientation of the WBZ451 Curiosity board using a pretrained TensorFlow model, and then sends the classification result to an MBD App.

This project presents a real-time face emotion detection system implemented as a full-stack desktop application. The system integrates deep learning techniques to accurately classify human emotions, including happiness, sadness, anger, surprise, disgust, fear, and neutral expressions, from live video streams or camera inputs. Download apk file :-