Google's ML Kit Object Detection and Tracking for Flutter

Pub Version

A Flutter plugin to use Google's ML Kit Object Detection and Tracking to detect and track objects in an image or live camera feed.

Getting Started

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

Firebase dependency

Object Detection and Tracking could be used with both Base Models and Custom Models. Base models are bundled with the app. Custom Models are downloaded from Firebase. Since both model options are handled in this plugin, that requires you to add Firebase to your project even if you are only using the Base Models. More details here.

To add Firebase to your project follow these steps:


Object Detection and Tracking

Create an instance of InputImage

Create an instance of InputImage as explained here.

final InputImage inputImage;

Create an instance of ObjectDetector

final objectDetector = ObjectDetector(options: CustomObjectDetectorOptions or ObjectDetectorOptions)

Process image

final List<DetectedObject> objects = await objectDetector.processImage(inputImage);

for(DetectedObject detectedObject in _objects){
  final rect = detectedObject.boundingBox;
  final trackingId = detectedObject.trackingId;

  for(Label label in detectedObject.labels){
    print('${label.text} ${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 = LocalObjectDetectorOptions(
  modelPath: modelPath,
  classifyObjects: true,
  multipleObjects: true,
final objectDetector = ObjectDetector(options: options);

Managing remote models

Create an instance of model manager

final modelManager = FirebaseObjectDetectorModelManager();

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.