Google's ML Kit Image Labeling for Flutter

Pub Version analysis Star on Github License: MIT

A Flutter plugin to use Google's ML Kit Image Labeling to detect and extract information about entities in an image across a broad group of categories.

Getting Started

Before you get started read about the requirements and known issues of this plugin here.

Firebase dependency for remote models

Image Labeling can be used with both Base Models and Custom Models. Base models are bundled with the app, and custom Models can either be bundled with the app or downloaded from Firebase.

If you wish to use remote models hosted in Firebase, you must first enable the feature in iOS. Please see the additional setup instructions here.

To add Firebase to your project follow these steps:


Image Labeling

Create an instance of InputImage

Create an instance of InputImage as explained here.

final InputImage inputImage;

Create an instance of ImageLabeler

final ImageLabelerOptions options = ImageLabelerOptions(confidenceThreshold: 0.5);
final imageLabeler = ImageLabeler(options: options);

Process image

final List<ImageLabel> labels = await imageLabeler.processImage(inputImage);

for (ImageLabel label in labels) {
  final String text = label.text;
  final int index = label.index;
  final double confidence = label.confidence;

Release resources with close()


Load local custom model

To use a local custom model add the tflite model to your pubspec.yaml:

- assets/ml/

Add this method:

import 'dart:io';
import 'package:flutter/services.dart';
import 'package:path/path.dart';
import 'package:path_provider/path_provider.dart';

Future<String> _getModel(String assetPath) async {
  if (io.Platform.isAndroid) {
    return 'flutter_assets/$assetPath';
  final path = '${(await getApplicationSupportDirectory()).path}/$assetPath';
  await io.Directory(dirname(path)).create(recursive: true);
  final file = io.File(path);
  if (!await file.exists()) {
    final byteData = await rootBundle.load(assetPath);
    await file.writeAsBytes(byteData.buffer
        .asUint8List(byteData.offsetInBytes, byteData.lengthInBytes));
  return file.path;

Create an instance of ImageLabeler:

final modelPath = await _getModel('assets/ml/object_labeler.tflite');
final options = LocalLabelerOptions(modelPath: modelPath);
final imageLabeler = ImageLabeler(options: options);

Managing remote models

Create an instance of model manager

final modelManager = FirebaseImageLabelerModelManager();

Check if model is downloaded

final bool response = await modelManager.isModelDownloaded(model);

Download model

final bool response = await modelManager.downloadModel(model);

Delete model

final bool response = await modelManager.deleteModel(model);

Example app

Find the example app here.


Contributions are welcome. In case of any problems look at existing issues, if you cannot find anything related to your problem then open an issue. Create an issue before opening a pull request for non trivial fixes. In case of trivial fixes open a pull request directly.