tensorflow_lite 0.0.3

tensorflow_lite: ^0.0.3

A Flutter plugin to access TensorFlow Lite apis.

tensorflow_lite

A Flutter plugin to access TensorFlow Lite apis. TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices. With TensorFlow Lite you can deploy machine learning models on phones in your Android/iOS app.

Usage

Add tensorflow_lite to your pubspec.yaml

Copy your models to an asset dir like assets/mobilenet_quant_v1_224.tflite And add it to your pubspec.yaml

   assets:
     - assets/mobilenet_quant_v1_224.tflite

Import tensorflow_lite in your app

import 'package:tensorflow_lite/tensorflow_lite.dart';

Create a new Interpreter instance based on your tflite model file

Interpreter model = await Interpreter.createInstance(modelFilePath: modelPath);

Pass some bytes to the model to get the output

dynamic result = await _interpreter.run(imageToByteList(image), new Uint8List(_labelList.length));

Image Classification example

tensorflow_lite also includes a wrapper for image classification models which can be easily loaded without much of boilerplate code.

Future<Null> loadRecognitions() async {
    var classifier = await TFLiteImageClassifier.createInstance(
      assets: rootBundle,
      modelPath: "assets/mobilenet_quant_v1_224.tflite",
      labelPath: "assets/labels.txt",
      inputSize: 224,
    );
    print('Classifier ready');
    var imageBytes = (await rootBundle.load("assets/cat500.png")).buffer;
    img.Image image = img.decodePng(imageBytes.asUint8List());
    image = img.copyResize(image, 224, 224);
    _recognitions = await classifier.recognizeImage(image);
    setState(() {});

    await classifier.close();
  }

Please check the example for full usage.

Note

• Works only on Android
• Tested only on image classification

Contributing

I am new to Flutter and I haven't worked on iOS yet. So if you are an iOS developer, i'd be glad to receive some contribution. Just send a PR or open up an issue!