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A Flutter plugin to use all APIs from Google's standalone ML Kit for mobile platforms.

Google's ML Kit for Flutter #

Pub Version analysis Star on Github License: MIT

Google's ML Kit for Flutter is a set of Flutter plugins that enable Flutter apps to use Google's standalone ML Kit.

google_ml_kit is an umbrella plugin that includes all the features listed below, it groups all the plugins under a single one. By using google_ml_kit you will include all the plugins listed below and their respective dependencies, therefore significantly increasing the size of your app. We recommend instead to add only the plugin for the feature you want to consume rather than including google_ml_kit in your yaml.

Go to each plugin listed bellow to read about their documentation and requirements. If you find issues report and contribute with your pull requests.

Features #

Vision APIs #

Feature Plugin Source Code Android iOS
Barcode Scanning google_mlkit_barcode_scanning Pub Version GitHub
Face Detection google_mlkit_face_detection Pub Version GitHub
Face Mesh Detection google_mlkit_face_mesh_detection Pub Version GitHub
Image Labeling google_mlkit_image_labeling Pub Version GitHub
Object Detection and Tracking google_mlkit_object_detection Pub Version GitHub
Text Recognition V2 google_mlkit_text_recognition Pub Version GitHub
Digital Ink Recognition google_mlkit_digital_ink_recognition Pub Version GitHub
Pose Detection google_mlkit_pose_detection Pub Version GitHub
Selfie Segmentation google_mlkit_selfie_segmentation Pub Version GitHub

Natural Language APIs #

Feature Plugin Source Code Android iOS
Language Identification google_mlkit_language_id Pub Version GitHub
On-Device Translation google_mlkit_translation Pub Version GitHub
Smart Reply google_mlkit_smart_reply Pub Version GitHub
Entity Extraction google_mlkit_entity_extraction Pub Version GitHub

PLEASE READ THIS before continuing or posting a new issue:

  • Google's ML Kit was build only for mobile platforms: iOS and Android apps.

  • This plugin is not sponsored or maintained by Google. The authors are developers excited about Machine Learning that wanted to expose Google's native APIs to Flutter.

  • Google's ML Kit APIs are only developed natively for iOS and Android. This plugin uses Flutter Platform Channels as explained here.

    Messages are passed between the client (the app/plugin) and host (platform) using platform channels as illustrated in this diagram:

    Messages and responses are passed asynchronously, to ensure the user interface remains responsive. To read more about platform channels go here.

    Because this plugin uses platform channels, no Machine Learning processing is done in Flutter/Dart, all the calls are passed to the native platform using MethodChannel in Android and FlutterMethodChannel in iOS, and executed using Google's native APIs. Think of this plugin as a bridge between your app and Google's native ML Kit APIs. This plugin only passes the call to the native API and the processing is done by Google's API. It is important that you understand this concept when it comes to debugging errors for your ML model and/or app.

  • Since the plugin uses platform channels, you may encounter issues with the native API. Before submitting a new issue, identify the source of the issue. You can run both iOS and/or Android native example apps by Google and make sure that the issue is not reproducible with their native examples. If you can reproduce the issue in their apps then report the issue to Google. The authors do not have access to the source code of their native APIs, so you need to report the issue to them. If you find that their example apps are okay and still you have an issue using this plugin, then look at our closed and open issues. If you cannot find anything that can help you then report the issue and provide enough details. Be patient, someone from the community will eventually help you.

Example app #

Find the example app here.

Contributing #

Contributions are welcome. In case of any problems look at existing issues, if you cannot find anything related to your problem then open an issue. Create an issue before opening a pull request for non trivial fixes. In case of trivial fixes open a pull request directly.