Announcement
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 me with questions until then.
Thanks!
- ptruiz@google.com
Overview
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.
Key Features
- 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.
(Important) Initial setup : Add dynamic libraries to your app
Android
Copy the folders from https://github.com/tensorflow/flutter-tflite/tree/main/releases/download/android into your project’s /android/app/src/main/jniLibs directory.
iOS
TODO: Sample now works, info soon
TFLite Flutter Helper Library
The helper library has been deprecated. New development underway for a replacement at https://github.com/google/flutter-mediapipe
Import
import 'package:tflite_flutter/tflite_flutter.dart';
Usage instructions
Creating the Interpreter
- From assetPlace
your_model.tfliteinassetsdirectory. Make sure to include assets inpubspec.yaml.final interpreter = await tfl.Interpreter.fromAsset(‘your_model.tflite’);
Refer to the documentation for info on creating interpreter from buffer or file.
Performing inference
- 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)
Closing the interpreter
interpreter.close();
Refer Tests to see more example code for each method.
Download and/or contribute this TensorFlow plugin on GitHub
Provides the list of the opensource Flutter apps collection with GitHub repository.