머신 러닝 용 플러터 사용 : Tensorflow Lite 및 Flutter 소개

Using Flutter for Machine Learning: An Introduction to TensorFlow Lite and Flutter

Flutter is an open-source mobile application development framework created by Google. It is used to develop applications for Android, iOS, Windows, Mac, Linux, Google Fuchsia, and the web from a single codebase. Flutter has become increasingly popular in recent years due to its ability to create high-performance, cross-platform applications with a beautiful user interface. One of the most exciting features of Flutter is its ability to be used for machine learning applications. In this blog post, we will explore how to use Flutter for machine learning by introducing TensorFlow Lite and how to integrate it with Flutter.

What is TensorFlow Lite?

TensorFlow Lite is a lightweight version of Google’s open-source machine learning library, TensorFlow. It is designed to be used on mobile and embedded devices with limited resources. TensorFlow Lite enables developers to deploy machine learning models on mobile and embedded devices with low latency and small binary size. It also supports hardware acceleration on both Android and iOS devices.

How to Use TensorFlow Lite with Flutter

Using TensorFlow Lite with Flutter is relatively straightforward. The first step is to install the TensorFlow Lite plugin for Flutter. This plugin provides a set of APIs that allow developers to easily use TensorFlow Lite in their Flutter applications.

Once the plugin is installed, developers can use the APIs to load a TensorFlow Lite model into their application. This can be done by using the tflite.loadModel() method. This method takes a path to the model file as an argument and returns a TfliteModel object.

Once the model is loaded, developers can use the tflite.run() method to run inference on the model. This method takes an array of input data and returns an array of output data.

Building a Machine Learning App with Flutter

Now that we have an understanding of how to use TensorFlow Lite with Flutter, let’s build a simple machine learning application. In this example, we will build an application that uses a pre-trained model to classify images.

The first step is to create a new Flutter project. Once the project is created, we can add the TensorFlow Lite plugin to our project. We can do this by adding the following line to our pubspec.yaml file:

dependencies:
  tflite: ^1.2.0

Once the plugin is installed, we can add the model file to our project. For this example, we will use a pre-trained model that can classify images of cats and dogs. We can add the model file to our project by adding the following line to our pubspec.yaml file:

assets:
  - assets/model.tflite

Once the model is added to our project, we can use the tflite.loadModel() method to load the model into our application. We can do this in the initState() method of our main page:

@override
void initState() {
  super.initState();
  _loadModel();
}

void _loadModel() async {
  String modelPath = 'assets/model.tflite';
  tflite.Model model = await tflite.loadModel(
    modelPath,
    labels: "assets/labels.txt",
  );
}

Once the model is loaded, we can use the tflite.run() method to run inference on the model. This method takes an array of input data and returns an array of output data. For this example, we will use an array of pixel values as our input data. We can do this in the runInference() method:

Future<List<dynamic>> runInference(Uint8List imageData) async {
  List<dynamic> output = await tflite.runModelOnBinary(
    binary: imageData,
    numResults: 2,
  );
  return output;
}

Once we have the output from the model, we can use it to classify the image. For this example, we will use the output to determine if the image is of a cat or a dog. We can do this in the classifyImage() method:

String classifyImage(List<dynamic> output) {
  if (output[0] > output[1]) {
    return 'cat';
  } else {
    return 'dog';
  }
}

Finally, we can use the classifyImage() method to classify the image. We can do this in the onImageSelected() method:

void onImageSelected(Uint8List imageData) async {
  List<dynamic> output = await runInference(imageData);
  String classification = classifyImage(output);
  setState(() {
    _classification = classification;
  });
}

Conclusion

In this blog post, we explored how to use Flutter for machine learning by introducing TensorFlow Lite and how to integrate it with Flutter. We also built a simple machine learning application that uses a pre-trained model to classify images. With TensorFlow Lite, developers can easily create powerful machine learning applications with Flutter.