# The TensorFlow Lite Plugin for Flutter is Officially Available

DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available](<https://devfeed.tech/articles/the-tensorflow-lite-plugin-for-flutter-is-officially-available-7385.md>)

Original publisher: [Read original article](<https://blog.tensorflow.org/2023/08/the-tensorflow-lite-plugin-for-flutter-officially-available.html>)

Author: TensorFlow Blog (noreply@blogger.com)

Published: 2023-08-18T16:00:00Z

Content type: news

Language: en

Sources: [The TensorFlow Blog](<https://devfeed.tech/sources/the-tensorflow-blog.md>)

Topics: [Flutter](<https://devfeed.tech/topics/flutter.md>), [TensorFlow Lite](<https://devfeed.tech/topics/tensorflow-lite.md>), [Tensorflow](<https://devfeed.tech/topics/tensorflow.md>), [cross-platform](<https://devfeed.tech/topics/cross-platform.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [object-detection](<https://devfeed.tech/topics/object-detection.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Google](<https://devfeed.tech/topics/google.md>), [Kaggle](<https://devfeed.tech/topics/kaggle.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [cross-platform](<https://devfeed.tech/tags/cross-platform.md>), [explore](<https://devfeed.tech/tags/explore.md>), [flutter](<https://devfeed.tech/tags/flutter.md>), [github](<https://devfeed.tech/tags/github.md>), [google](<https://devfeed.tech/tags/google.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kaggle](<https://devfeed.tech/tags/kaggle.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [plugin](<https://devfeed.tech/tags/plugin.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tensorflow-lite](<https://devfeed.tech/tags/tensorflow-lite.md>), [tensorflowlite](<https://devfeed.tech/tags/tensorflowlite.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

## AI overview

The TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released. The updated plugin adds features and example apps, including live-camera object detection, and enables local TensorFlow model inference in Flutter apps across supported platforms.

## Source excerpt

Posted by Paul Ruiz, Developer Relations Engineer We're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to our official repo, making it easier to maintain directly by the Google team. We are grateful to Amish for his contributions to the TensorFlow Lite Flutter plugin. Through the efforts of developers in the community, the plugin has been updated to the latest version of TensorFlow Lite, and a collection of new features and example apps have been added, such as object detection through a live camera feed. So what is TensorFlow Lite? TensorFlow Lite is a way to run TensorFlow models on devices locally, supporting mobile, embedded, web, and edge devices. TensorFlow Lite's cross-platform support and on-device performance optimizations make it a great addition to the Flutter development toolbox. Our goal with this plugin is to make it easy to integrate TensorFlow Lite models into Flutter apps across mobile platforms, with desktop support currently in development through the efforts of our developer community. Find pre-trained TensorFlow Lite models on model repos like Kaggle Models or create your own custom TensorFlow Lite models. Let's take a look at how you could use the Flutter TensorFlow Lite plugin for image classification: TensorFlow Lite Image Classification with Flutter First you will need to install the plugin from pub.dev. Once the plugin is installed, you can load a TensorFlow Lite model into your Flutter app and define the input and output tensor shapes. If you're using the MobileNet model, then the input tensor will be a 224 by 224 RGB image, and the output will be a list of confidence scores for the trained labels. // Load model Future<void> _loadModel() async { final opt