How to Implement Tensorflow lite in Flutter

How to Implement Tensorflow lite in Flutter

Machine learning and AI are taking mobile application development to a new level. Apps that utilizing machine learning can recognize speech, images, and gestures. This gives us new and compelling ways to engage and interact with people in the world around us. But how do we integrate machine learning into our mobile apps? Ever since I heard about TensorFlow Lite I wanted to create an app to test the power of the machine learning model on Android devices. This tool provides us with pre-trained machine learning models as well as tools to train and import our own custom models. But how do we actually develop a compelling experience on top of those machine learning models? That’s where Flutter comes in. Learn about how to work with TensorFlow in Flutter

Introduction

Machine learning and AI are taking mobile application development to a new level. Apps that utilizing machine learning can recognize speech, images, and gestures. This gives us new and compelling ways to engage and interact with people in the world around us. But how do we integrate machine learning into our mobile apps?

Ever since I heard about TensorFlow Lite I wanted to create an app to test the power of the machine learning model on Android devices. This tool provides us with pre-trained machine learning models as well as tools to train and import our own custom models. But how do we actually develop a compelling experience on top of those machine learning models? That’s where Flutter comes in.

We will create a project which will classify the Color by showing confidence and labels.

Prerequisite

  • Trained model (TensorFlow lite).
  • Flutter.
  • Tensorflow lite (tflite package).
  • percent_indicator: ^2.1.1+1
  • camera: ^0.5.7+4

Create a new flutter Project

  • You can use IDE or CMD. and remove all the boilerplate code from the main.dart file.
flutter create project_name

tensorflow-lite artificial-intelligence flutter machine-learning

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