A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources.

Buy Now is an e-commerce app. In this app I use Machine Learning Algorithm for users can search visually. Users upload or take a photo, and this Algorithm will search for a similar product.

End-to-end tutorials on how to create TensorFlow Lite (TFLite) models!

Medical Corner is a medical application that detection of pneumonia and brain tumor. It also provides prediction for heart disease, and diabetes.

License plate recognition . Model training and conversion to tflite

Introductory tutorial on converting a TensorFlow model to TFLite model

American Sign Language Detection is a deep learning end to end project where we can detect American Sign Language.

Android application for Indian license plate recognition using deep learning (Tensorflow) and OpenCV, and extraction of vehicle details from the Vahan website.

A flutter app to check whether person wear a mask or not

Face Mask Detection using OpenCV and caffee to face detection

This repo represents our machine learning project Image Description which is used to generate a description of an image based on activities and objects detected in the image.

This app helps to boost mental health by stress detection and helping ways to reduce it. made for Google solution Chalenge.

Some basic ML models in a single Android Application.

How to convert h5 model to tflite model

TensorFlow Lite Segementation example in Python

A tensorflow application with 11 different types of image classification (4 for binary and 7 for multiclass classifications).

A cat dog classifier in Flutter

This is a Flutter +TFlite project made to detect stress in image and provide ways to calm yourself.

A project that can compose an image into a cartoon or the style of another image by using pre-trained deep learning models.