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 moreHow do I create a realtime database in Firebase?
Create a Database
Read moreHow do I start working with Firebase?
Getting started with Firebase
Read moreCan ml kit be used offline?
ML Kit’s APIs all run on-device, allowing for real-time use cases where you want to process a live camera stream for example. This also means that the functionality is available offline .
Read moreHow do you make a model in python?
Model Creation in Python
Read moreWhat is custom ML model?
By default, ML Kit’s APIs make use of Google trained machine learning models. These models are designed to cover a wide range of applications . However, some use cases require models that are more targeted.
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