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  • Writer's pictureRobin Alex Panicker

A Guide to Utilizing Machine Learning Features of Flutter

Updated: Jun 27

A Guide to Utilizing Machine Learning Features of Flutter

Machine learning is revolutionizing mobile app development, enabling intelligent decision-making and enhancing user experiences. Flutter, the open-source UI toolkit from Google, offers a robust set of tools and libraries to seamlessly integrate machine learning capabilities into your applications.

In this blog post, we will dive into the practical aspects of utilizing Flutter's machine learning features, accompanied by relevant code samples.

1. Understanding Machine Learning Capabilities of Flutter

Flutter provides various machine learning options, including TensorFlow Lite, ML Kit, and community packages. These options allow developers to integrate machine learning models into their Flutter apps, leveraging pre-trained models or building custom models tailored to specific use cases.

2. Using TensorFlow Lite with Flutter

TensorFlow Lite is a lightweight framework for deploying machine learning models on mobile and embedded devices.

Let's explore how to use TensorFlow Lite with Flutter:

2.1 Model Selection

Choose a pre-trained TensorFlow Lite model or build a custom model using TensorFlow. Convert the model to TensorFlow Lite format. TensorFlow Hub is a great resource for finding pre-trained models for tasks like image recognition or natural language processing.

2.2 Integration

Add the TensorFlow Lite dependency to your Flutter project's pubspec.yaml file:

dependencies:flutter:sdk: flutter
tflite: ^X.X.X  
# Replace with the latest version

2.3 Model Loading

Load the TensorFlow Lite model into your Flutter app using the TensorFlow Lite Flutter package. You can load the model from an asset file or a remote location:

import 'package:tflite/tflite.dart';

// Load the TensorFlow Lite model
await Tflite.loadModel(
  model: 'assets/model.tflite',
  labels: 'assets/labels.txt',

2.4 Model Inference

Perform inference with the loaded TensorFlow Lite model using input data and receive predictions or results:

List<dynamic> inference = await Tflite.runModelOnImage(
  path: 'path_to_image.jpg',
  numResults: 5,

// Process the inference results
inference.forEach((result) {
  final label = result['label'];
  final confidence = result['confidence'];
  print('Label: $label, Confidence: $confidence');

3. Leveraging ML Kit for Flutter

ML Kit is a suite of machine learning capabilities provided by Google, simplifying the integration of machine learning models into mobile apps. Let's see how to use ML Kit with Flutter:

3.1 Integration

Add the ML Kit Flutter package as a dependency to your pubspec.yaml file:

dependencies:flutter:sdk: flutter
firebase_ml_vision: ^X.X.X  
# Replace with the latest version

3.2 Model Selection

Choose the ML Kit model that suits your application requirements. For example, to incorporate text recognition, use the Text Recognition API.

3.3 Model Configuration

Configure the ML Kit model by specifying parameters such as language support, confidence thresholds, and other options.

3.4 Integration and Inference

Integrate the model into your app and perform inference using the ML Kit Flutter package:

import 'package:firebase_ml_vision/firebase_ml_vision.dart';

// Initialize the text recognizer
final textRecognizer = FirebaseVision.instance.textRecognizer();

// Process an image and extract text
final FirebaseVisionImage visionImage = FirebaseVisionImage.fromFilePath('path_to_image.jpg');
final VisionText visionText = await textRecognizer.processImage(visionImage);

// Extract text from the VisionText object
final extractedText = visionText.text;

// Perform additional processing with the extracted text
// ...

4. Exploring Flutter Community Packages

In addition to TensorFlow Lite and ML Kit, the Flutter community has developed various packages providing machine learning functionalities. These packages cover areas like natural language processing, image processing, recommendation systems, etc. Popular community packages include tflite_flutter, flutter_tflite, and flutter_native_image.

5. Custom Machine Learning Models with Flutter

If the available pre-trained models do not meet your specific requirements, you can build custom machine learning models using TensorFlow or other frameworks. Once trained and optimized, convert your model to TensorFlow Lite format and integrate it into your Flutter app using the steps outlined in Section 2.


Flutter's machine learning capabilities empower developers to create intelligent and feature-rich mobile applications.

By leveraging TensorFlow Lite, ML Kit, or community packages, you can seamlessly integrate machine learning models into your Flutter apps. The provided code samples serve as a starting point for your exploration of Flutter's machine learning features, opening up a realm of possibilities for creating innovative and smart mobile applications.

Blog for Mobile App Developers, Testers and App Owners


This blog is from Finotes Team. Finotes is a lightweight mobile APM and bug detection tool for iOS and Android apps.

In this blog we talk about iOS and Android app development technologies, languages and frameworks like Java, Kotlin, Swift, Objective-C, Dart and Flutter that are used to build mobile apps. Read articles from Finotes team about good programming and software engineering practices, testing and QA practices, performance issues and bugs, concepts and techniques. 

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