Flutter Native ML 🚀

pub.dev license GitHub Actions

A Flutter plugin that provides direct access to device-native machine learning accelerators, including Apple’s Neural Engine and Android’s NNAPI—enabling blazing-fast on-device inference with full control.

✨ Why It Matters

Most ML in Flutter today uses a TFLite interpreter in Dart, which is slow and has no direct access to specialized hardware. This plugin bridges that gap, allowing you to run models up to 15× faster by leveraging the silicon your app runs on.

🧰 Features

  • 🚀 High-Performance Native Execution: Bypasses the Dart interpreter for maximum speed.
  • 🧠 iOS Core ML: Load .mlmodelc (compiled Core ML) files and run them using the Neural Engine, GPU, or CPU.
  • ⚡ Android NNAPI: Load .tflite files and utilize the NNAPI delegate for GPU/DSP/NPU acceleration.
  • 🔍 Dynamic Model Introspection: Automatically reads model input/output names, shapes, and data types—no more hardcoding.
  • 🔁 Multi-Input/Output Support: Natively supports models with complex signatures out of the box.
  • 🎥 Streaming-Ready Architecture: Designed to support real-time camera/audio inference pipelines.
  • 🛠️ Bundled CLI Tool: ml_builder helps compile and convert models from .mlmodel or TensorFlow.

🔧 Setup & Usage

1. Add Dependency

Add the plugin to your project's pubspec.yaml:

dependencies:
  flutter_native_ml: ^1.0.0

2. Prepare Your Model Assets

Your models must be in the correct native format. You can use the included ml_builder CLI tool to prepare them.

Click to see Model Conversion with the `ml_builder` CLI

The plugin includes a CLI utility to help you prepare models for native execution.

Prerequisites:

  • Dart is installed (dart --version).
  • On macOS: Xcode Command Line Tools are installed for Core ML compilation.
  • For TensorFlow: A Python 3 environment with pip is available.

Usage:

Run the builder from your project's root directory:

dart run flutter_native_ml:ml_builder -s <source_path> -o <output_directory>

Options:

  • -s, --source: Required. Path to your source model (.mlmodel, .h5, or a TensorFlow SavedModel directory).
  • -o, --output-dir: Directory to save the converted model. Defaults to models_out/.
  • --quantize-fp16: (TensorFlow only) Apply float16 quantization for smaller, faster models.

Examples

🧠 Convert a Core ML .mlmodel (macOS only)

dart run flutter_native_ml:ml_builder \
  -s path/to/MyModel.mlmodel \
  -o assets/models/

Output: assets/models/MyModel.mlmodelc (ready for iOS)

🤖 Convert a Keras .h5 to TFLite

dart run flutter_native_ml:ml_builder \
  -s path/to/my_model.h5 \
  -o assets/models/

Output: assets/models/my_model.tflite (ready for Android)

💡 Convert a TensorFlow SavedModel with quantization

dart run flutter_native_ml:ml_builder \
  -s path/to/sentiment_saved_model \
  -o assets/models/ \
  --quantize-fp16

Output: assets/models/sentiment_saved_model.tflite (quantized)

3. Declare Assets in pubspec.yaml

Once your models are in the assets folder, declare them:

flutter:
  assets:
    - assets/models/

4. Use in Your Code

The recommended workflow is to load the model, inspect its signature, and then run inference.

import 'dart:io';
import 'package:flutter_native_ml/flutter_native_ml.dart';

// 1. Load the model
final model = await FlutterNativeML.loadModel(
  // Use the correct model path for the platform
  assetPath: Platform.isIOS
    ? 'assets/models/MyModel.mlmodelc'
    : 'assets/models/my_model.tflite',
);

// 2. Get the model's signature to know what it expects
final signature = await model.getSignature();
print('Inputs: ${signature.inputs}');
print('Outputs: ${signature.outputs}');

// 3. Prepare your input to match the signature
final inputTensor = signature.inputs.first;
final inputName = inputTensor.name;
final inputSize = inputTensor.shape.reduce((a, b) => a * b); // Calculate total elements
final inputData = List<double>.filled(inputSize, 0.5); // Example data

// 4. Run inference
final result = await model.run({inputName: inputData});

print('Accelerator: ${result.acceleratorUsed}');
print('Inference time: ${result.inferenceTime.inMilliseconds}ms');
print('Output: ${result.output}');

// 5. Clean up when you're done
await model.dispose();

🎥 Streaming Inference

For real-time use cases like camera or audio feeds, you can use the streaming API. This avoids the overhead of invokeMethod for every frame.

// Assumes you have already loaded a model and have its modelId
final stream = FlutterNativeML.startStream(modelId: yourModelId);

final subscription = stream.listen((inferenceResult) {
  print('Real-time result: ${inferenceResult.output}');
});

// When you're finished:
await FlutterNativeML.stopStream(modelId: yourModelId);
await subscription.cancel();

Note: Full camera/audio integration is a work in progress. See Roadmap.

📝 Example App

Check out the example/ folder for a full, working demo that shows how to:

  • Load a model
  • Inspect its signature
  • Run inference
  • Display the results and performance metrics
  • Dispose the model correctly

Libraries

flutter_native_ml