# TensorflowLite Published articles for TensorflowLite. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What's new in TensorFlow 2.19 DevFeed: [What's new in TensorFlow 2.19]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2025-03-13T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [LiteRT](), [releases](), [API](), [C++](), [Release notes](), [Keras](), [migration]() Tags: [2025](), [announcement](), [api](), [c-plus-plus](), [keras](), [libentensorflow](), [litert](), [migration-guide](), [pypi](), [release](), [release-notes](), [tensorflow](), [tensorflow-core](), [tensorflowlite]() ### AI overview TensorFlow 2.19 introduces C++ API changes in LiteRT, adds bfloat16 support for the tflite casting operation, and deprecates tf.lite.Interpreter in favor of ai_edge_litert.interpreter. The release also stops publishing libtensorflow packages, although they remain extractable from the PyPI package. Updates for multi-backend Keras are directed to keras.io. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.19 has been released! Highlights of this release include changes to the C++ API in LiteRT, bfloat16 support for tflite casting, discontinue of releasing libtensorflow packages. Learn more by reading the full release notes. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core LiteRT The public constants tflite::Interpreter:kTensorsReservedCapacity and tflite::Interpreter:kTensorsCapacityHeadroom are now const references, rather than constexpr compile-time constants. (This is to enable better API compatibility for TFLite in Play services while preserving the implementation flexibility to change the values of these constants in the future.) TF-Lite tfl.Cast op is now supporting bfloat16 in the runtime kernel. tf.lite.Interpreter gives a deprecation warning redirecting to its new location at ai_edge_litert.interpreter, as the API tf.lite.Interpreter will be deleted in TF 2.20. See the migration guide for details. Libtensorflow We have stopped publishing libtensorflow packages but it can still be unpacked from the PyPI package. ## The TensorFlow Lite Plugin for Flutter is Officially Available DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-18T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Tensorflow](), [cross-platform](), [Mobile](), [object-detection](), [GitHub](), [Google](), [Kaggle]() Tags: [announcement](), [cross-platform](), [explore](), [flutter](), [github](), [google](), [inference](), [kaggle](), [mobile](), [object-detection](), [plugin](), [tensorflow](), [tensorflow-lite](), [tensorflowlite](), [tutorial]() ### 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 _loadModel() async { final opt