On-Device AI in Flutter 2026: TensorFlow Lite Tutorial (Image Classifier App) 🤖

If you’ve ever wanted AI in your Flutter app without Gemini API costs, this is it.

Hey guys! It’s Samuel once again. Today, we’re going to be integrating something cool: On-device AI: no cloud, no internet — just pure privacy and speed with TensorFlow Lite. We will use TensorFlow Lite to build an image classifier that recognizes objects in real-time from your camera or gallery.

Why on-device in 2026? Cloud AI is great, but on-device wins for offline apps, privacy (no data sent), and battery life. Flutter’s ecosystem is mature. We have tflite_flutter that handles delegates beautifully now.

We’ll use MobileNet (quantized for mobile), download the .tflite model and labels.txt from TensorFlow Hub.

New project: flutter create ai_classifier.

Create a new assets folder in your project directory: assets/models, and download the mobilenet file and labels.text file and place it inside the assets/models folder.

Next, install dependencies in pubspec.yaml:

dependencies:
  flutter_riverpod: ^3.2.0
  google_fonts: ^7.1.0
  camera: ^0.11.3
  image_picker: ^1.2.1
  tflite_flutter: ^0.12.1
  path_provider: ^2.1.5
  permission_handler: ^12.0.1
  path: ^1.9.1
  image: ^4.7.2

assets:
  - assets/models/mobilenet_v1.tflite
  - assets/models/labels.txt

TFLite helper: lib/providers/classifier_provider.dart

import ‘package:flutter_riverpod/flutter_riverpod.dart’;
import ‘../services/tflite_service.dart’;

final tfliteServiceProvider = Provider<TfliteService>((ref) {
  return TfliteService();
});

final classifierInitializedProvider = FutureProvider<void>((ref) async {
  final service = ref.watch(tfliteServiceProvider);
  await service.init();
});

Tflite Service: lib/services/tflite_service.dart

import ‘package:tflite_flutter/tflite_flutter.dart’;
import ‘package:image/image.dart’ as img;

class TfliteService {
  late Interpreter _interpreter;
  late List<String> _labels;

  Future<void> loadModel() async {
    _interpreter = await Interpreter.fromAsset(’mobilenet_v2.tflite’);
    final labelsData = await rootBundle.loadString(’assets/labels.txt’);
    _labels = labelsData.split(’\n’);
  }

  Future<List<Map<String, dynamic>>> predict(img.Image image) async {
    // Preprocess: Resize to 224x224, normalize
    final input = _preprocess(image);

    final output = List.filled(1 * 1001, 0.0).reshape([1, 1001]);

    _interpreter.run(input, output);

    // Postprocess: Top 5 labels
    final List<Map<String, dynamic>> results = [];
    final outputList = output[0] as List<double>;
    for (int i = 0; i < outputList.length; i++) {
      results.add({’index’: i, ‘confidence’: outputList[i]});
    }
    results.sort((a, b) => b[’confidence’].compareTo(a[’confidence’]));
    return results.take(5).map((r) => {
      ‘label’: _labels[r[’index’] as int],
      ‘confidence’: (r[’confidence’] as double * 100).toStringAsFixed(1),
    }).toList();
  }

  // Full preprocess function (resize, normalize to [0,1] or [-1,1] per model)
  // Use img package or custom
}

UI: Home screen with ImagePicker button.

Pick image → decode → run predict → display ListView of labels/confidence.

Add loading spinner + error handling.

 File? _selectedImage;
  List<Map<String, dynamic>>? _results;
  bool _isCameraMode = false;
  final ImagePicker _picker = ImagePicker();

  Future<void> _pickImage(ImageSource source) async {
    final XFile? image = await _picker.pickImage(source: source);
    if (image != null) {
      setState(() {
        _selectedImage = File(image.path);
        _results = null;
      });
      _classifyImage(_selectedImage!);
    }
  }

  Future<void> _classifyImage(File file) async {
    final service = ref.read(tfliteServiceProvider);
    final results = await service.classifyImage(file);
    setState(() {
      _results = results;
    });
  }

That’s all! Demo on emulator/real device, pick a photo, see instant labels, and see how it works.

Full Source Code 👇 — Show some ❤️ by starring ⭐ the repo and follow me 😄!

https://github.com/techwithsam/vision_ai

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