Update: 26 April, 2023

This repo is a TensorFlow managed fork of the tflite_flutter_plugin project by the amazing Amish Garg. The goal of this project is to support our Flutter community in creating machine-learning backed apps with the TensorFlow Lite framework.

This project is currently a work-in-progress as we update it to create a working plugin that meets the latest and greatest Flutter and TensorFlow Lite standards. That said, pull requests and contributions are more than welcome and will be reviewed by TensorFlow or Flutter team members. We thank you for your understanding as we make progress on this update.

Feel free to reach out to us by posting in the issues or discussion areas.

Thanks!

• PaulTR

TensorFlow Lite Flutter plugin provides a flexible and fast solution for accessing TensorFlow Lite interpreter and performing inference. The API is similar to the TFLite Java and Swift APIs. It directly binds to TFLite C API making it efficient (low-latency). Offers acceleration support using NNAPI, GPU delegates on Android, Metal and CoreML delegates on iOS, and XNNPack delegate on Desktop platforms.

• Multi-platform Support for Android and iOS
• Flexibility to use any TFLite Model.
• Acceleration using multi-threading.
• Similar structure as TensorFlow Lite Java API.
• Inference speeds close to native Android Apps built using the Java API.
• Run inference in different isolates to prevent jank in UI thread.

Examples and support now support dynamic library downloads! iOS samples can be run with the commands

flutter build ios & flutter install ios from their respective iOS folders.

Android can be run with the commands

flutter build android & flutter install android

while devices are plugged in.

Note: This requires a device with a minimum API level of 26.

Note: TFLite may not work in the iOS simulator. It's recommended that you test with a physical device.

When creating a release archive (IPA), the symbols are stripped by Xcode, so the command flutter build ipa may throw a Failed to lookup symbol ... symbol not found error. To work around this:

1. In Xcode, go to Target Runner > Build Settings > Strip Style
2. Change from All Symbols to Non-Global Symbols

For MacOS a TensorFlow Lite dynamic library needs to be added to the project manually. For this, first a .dylib needs to be built. You can follow the Bazel build guide or the CMake build guide to build the libraries.

CMake Note:

• cross compiling in CMake can be achieved using: -DCMAKE_OSX_ARCHITECTURES=x86_64|arm64

• bundling two architectures (arm / x86) using lipo: lipo -create arm64/libtensorflowlite_c.dylib x86/libtensorflowlite_c.dylib -output libtensorflowlite_c.dylib

As a second step, the library needs to be added to your application's XCode project. For this, you can follow the step 1 and 2 of the official Flutter guide on adding dynamic libraries.

For Linux a TensorFlow Lite dynamic library needs to be added to the project manually. For this, first a .so needs to be built. You can follow the Bazel build guide or the CMake build guide to build the libraries.

As a second step, the library needs to be added to your application's project. This is a simple procedure

1. Create a folder called blobs in the top level of your project
2. Copy the libtensorflowlite_c-linux.so to this folder
3. Append following lines to your linux/CMakeLists.txt

...

# get tf lite binaries
install(
  FILES ${PROJECT_BUILD_DIR}/../blobs/libtensorflowlite_c-linux.so
  DESTINATION ${INSTALL_BUNDLE_DATA_DIR}/../blobs/
)

For Windows a TensorFlow Lite dynamic library needs to be added to the project manually. For this, first a .dll needs to be built. You can follow the Bazel build guide or the CMake build guide to build the libraries.

As a second step, the library needs to be added to your application's project. This is a simple procedure

1. Create a folder called blobs in the top level of your project
2. Copy the libtensorflowlite_c-win.dll to this folder
3. Append following lines to your windows/CMakeLists.txt

...

# get tf lite binaries
install(
  FILES ${PROJECT_BUILD_DIR}/../blobs/libtensorflowlite_c-win.dll 
  DESTINATION ${INSTALL_BUNDLE_DATA_DIR}/../blobs/
)

The helper library has been deprecated. New development underway for a replacement at https://github.com/google/flutter-mediapipe. Current timeline is to have wide support by the end of August, 2023.

import 'package:tflite_flutter/tflite_flutter.dart';

In the dependency section of pubspec.yaml file, add tflite_flutter: ^0.10.1 (adjust the version accordingly based on the latest release)

• From asset

  Place your_model.tflite in assets directory. Make sure to include assets in pubspec.yaml.

  final interpreter = await Interpreter.fromAsset('assets/your_model.tflite');

Refer to the documentation for info on creating interpreter from buffer or file.

• For single input and output

  Use void run(Object input, Object output).

  // For ex: if input tensor shape [1,5] and type is float32
  var input = [[1.23, 6.54, 7.81, 3.21, 2.22]];
  
  // if output tensor shape [1,2] and type is float32
  var output = List.filled(1*2, 0).reshape([1,2]);
  
  // inference
  interpreter.run(input, output);
  
  // print the output
  print(output);

• For multiple inputs and outputs

  Use void runForMultipleInputs(List<Object> inputs, Map<int, Object> outputs).

  var input0 = [1.23];  
  var input1 = [2.43];  
  
  // input: List<Object>
  var inputs = [input0, input1, input0, input1];  
  
  var output0 = List<double>.filled(1, 0);  
  var output1 = List<double>.filled(1, 0);
  
  // output: Map<int, Object>
  var outputs = {0: output0, 1: output1};
  
  // inference  
  interpreter.runForMultipleInputs(inputs, outputs);
  
  // print outputs
  print(outputs)

interpreter.close();

To utilize asynchronous inference, first create your Interpreter and then wrap it with IsolateInterpreter.

final interpreter = await Interpreter.fromAsset('assets/your_model.tflite');
final isolateInterpreter =
        await IsolateInterpreter.create(address: interpreter.address);

Both run and runForMultipleInputs methods of isolateInterpreter are asynchronous:

await isolateInterpreter.run(input, output);
await isolateInterpreter.runForMultipleInputs(inputs, outputs);

By using IsolateInterpreter, the inference runs in a separate isolate. This ensures that the main isolate, responsible for UI tasks, remains unblocked and responsive.

This package is managed using melos. Before starting to work on the project, make sure to run the bootstrap command.

dart pub global activate melos # Install or activate melos globally
melos bootstrap # Initialize the workspace and bootstrap the package

This package uses ffigen to generate FFI bindings. To run code generation, you can use the following melos command:

melos run ffigen