If you’re a Flutter developer, there’s exciting news on the horizon! Google has officially released the TensorFlow Lite plugin for Flutter, bringing the power of machine learning to your Flutter apps. This plugin, which has recently been migrated to the TensorFlow GitHub account, opens up a world of possibilities for creating intelligent and interactive applications.
The journey of the TensorFlow Lite plugin for Flutter started three years ago when Amish Garg, a Google Summer of Code contributor, developed a TensorFlow Lite plugin that quickly gained popularity. Recognizing its potential, Google has now taken over the maintenance and further development of the plugin, ensuring seamless integration and improved support.
TensorFlow Lite is a technology that allows TensorFlow machine learning models to run locally on devices. This means that the intelligence of machine learning models can be embedded directly into mobile devices, embedded systems, web applications, and edge devices. The exciting part is that TensorFlow Lite is now joining forces with Flutter, Google’s UI toolkit for building natively compiled applications for mobile, web, and desktop from a single codebase.
One of the remarkable features of the TensorFlow Lite plugin is its support for image classification. Imagine building an app that can identify objects in images captured through a live camera feed. This capability opens doors to a wide range of applications, from augmented reality experiences to automated image sorting.
An example of how to perform it is available on the official announcement page here: The TensorFlow Lite Plugin for Flutter is Officially Available
The journey doesn’t end here. The official GitHub repository for the TensorFlow Lite plugin for Flutter is a treasure trove of examples. You can explore text classification, super resolution, style transfer, and more. The possibilities are limited only by your creativity.
Moreover, Google is actively working on a new plugin tailored for MediaPipe Tasks. This plugin simplifies common on-device machine learning tasks, including image classification, object detection, audio classification, face landmark detection, and gesture recognition.
With these tools at your disposal, the sky’s the limit for the innovative applications you can build.