You can retrain the model using frozen graphs and TFLite models . If you get new training data and want to update the model in your app, try hosting it using Firebase MLKit.
Read moreHow do you verify a TFLite model?
You may use TensorFlow Lite Python interpreter to test your tflite model. It allows you to feed input data in python shell and read the output directly like you are just using a normal tensorflow model.
Read moreWhat models does TFLite support?
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile devices. It works cross-Platform and is supported on Java, C++ (WIP), and Swift (WIP) .
Read moreHow do I create a TensorFlow model?
Create your model
Read moreWhat is TFLite model maker?
It uses transfer learning to reduce the amount of training data required and shorten the training time. This guide walks you through creating a custom object detector and deploying it on Android.
Read moreCan you train a TFLite model?
This tutorial shows you how to construct a TensorFlow Lite model that can be incrementally trained and improved within an installed Android app. Note: The on-device training technique can be added to existing TensorFlow Lite implementations, provided the devices you are targeting support local file storage.
Read moreWhat is TFLite model?
TensorFlow Lite is Google’s machine learning framework to deploy machine learning models on multiple devices and surfaces such as mobile (iOS and Android), desktops and other edge devices .
Read more