Development tools for Android

TensorFlow Lite provides a number of tools for integrating models into Android apps. This page describes development tools for use in building apps with Kotlin, Java, and C++, as well as support for TensorFlow Lite development in Android Studio.

To get started quickly writing Android code, see the Quickstart for Android

Tools for building with Kotlin and Java

The following sections describe development tools for TensorFlow Lite that use the Kotlin and Java languages.

TensorFlow Lite Task Library

TensorFlow Lite Task Library contains a set of powerful and easy-to-use task-specific libraries for app developers to build with TensorFlow Lite. It provides optimized out-of-box model interfaces for popular machine learning tasks, such as image classification, question and answer, etc. The model interfaces are specifically designed for each task to achieve the best performance and usability. Task Library works cross-platform and is supported on Java and C++.

To use the Task Library in your Android app, use the AAR from MavenCentral for Task Vision library , Task Text library and Task Audio Library , respectively.

You can specify this in your build.gradle dependencies as follows:

dependencies {
    implementation 'org.tensorflow:tensorflow-lite-task-vision:+'
    implementation 'org.tensorflow:tensorflow-lite-task-text:+'
    implementation 'org.tensorflow:tensorflow-lite-task-audio:+'

If you use nightly snapshots, make sure you add the Sonatype snapshot repository to your project.

See the introduction in the TensorFlow Lite Task Library overview for more details.

TensorFlow Lite library

Use the TensorFlow Lite library in your Android app by adding the AAR hosted at MavenCentral to your development project.

You can specify this in your build.gradle dependencies as follows:

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:+'

If you use nightly snapshots, make sure you add the Sonatype snapshot repository to your project.

This AAR includes binaries for all of the Android ABIs. You can reduce the size of your application's binary by only including the ABIs you need to support.

Unless you are targeting specific hardware, you should omit the x86, x86_64, and arm32 ABIs in most cases. You can configure this with the following Gradle configuration. It specifically includes only armeabi-v7a and arm64-v8a, and should cover most modern Android devices.

android {
    defaultConfig {
        ndk {
            abiFilters 'armeabi-v7a', 'arm64-v8a'

To learn more about abiFilters, see Android ABIs in the Android NDK documentation.

TensorFlow Lite Support Library

The TensorFlow Lite Android Support Library makes it easier to integrate models into your application. It provides high-level APIs that help transform raw input data into the form required by the model, and interpret the model's output, reducing the amount of boilerplate code required.

It supports common data formats for inputs and outputs, including images and arrays. It also provides pre- and post-processing units that perform tasks such as image resizing and cropping.

Use the Support Library in your Android app by including the TensorFlow Lite Support Library AAR hosted at MavenCentral.

You can specify this in your build.gradle dependencies as follows:

dependencies {
    implementation 'org.tensorflow:tensorflow-lite-support:+'

If you use nightly snapshots, make sure you add the Sonatype snapshot repository to your project.

For instructions on how to get started, see the TensorFlow Lite Android Support Library.

Minimum Android SDK versions for libraries

Library minSdkVersion Device Requirements
tensorflow-lite 19 NNAPI usage requires API 27+
tensorflow-lite-gpu 19 GLES 3.1 or OpenCL (typically only available on API 21+
tensorflow-lite-hexagon 19 -
tensorflow-lite-support 19 -
tensorflow-lite-task-vision 21 related API requires API 26+
tensorflow-lite-task-text 21 -
tensorflow-lite-task-audio 23 -
tensorflow-lite-metadata 19 -

Using Android Studio

In addition to the development libraries described above, Android Studio also provides support for integrating TensorFlow Lite models, as described below.

Android Studio ML Model Binding

The ML Model Binding feature of Android Studio 4.1 and later allows you to import .tflite model files into your existing Android app, and generate interface classes to make it easier to integrate your code with a model.

To import a TensorFlow Lite (TFLite) model:

  1. Right-click on the module you would like to use the TFLite model or click on File > New > Other > TensorFlow Lite Model.

  2. Select the location of your TensorFlow Lite file. Note that the tooling configures the module's dependency with ML Model binding and automatically adds all required dependencies to your Android module's build.gradle file.

  3. Click Finish to begin the import process. When the import is finished, the tool displays a screen describing the model, including its input and output tensors.

  4. To start using the model, select Kotlin or Java, copy and paste the code in the Sample Code section.

You can return to the model information screen by double clicking the TensorFlow Lite model under the ml directory in Android Studio. For more information on using the Modle Binding feature of Android Studio, see the Android Studio release notes. For an overview of using model binding in Android Studio, see the code example instructions.

Tools for building with C and C++

The C and C++ libraries for TensorFlow Lite are primarily intended for developers using the Android Native Development Kit (NDK) to build their apps. There are two ways to use TFLite through C++ if you build your app with the NDK:


Using this API is the recommended approach for developers using the NDK. Download the TensorFlow Lite AAR hosted at MavenCentral file, rename to tensorflow-lite-*.zip, and unzip it. You must include the four header files in the headers/tensorflow/lite/ and headers/tensorflow/lite/c/ folders and the relevant dynamic library in the jni/ folder in your NDK project.

The c_api.h header file contains basic documentation about using the TFLite C API.

TFLite C++ API

If you want to use TFLite through C++ API, you can build the C++ shared libraries:

32bit armeabi-v7a:

bazel build -c opt --config=android_arm //tensorflow/

64bit arm64-v8a:

bazel build -c opt --config=android_arm64 //tensorflow/

Currently, there is no straightforward way to extract all header files needed, so you must include all header files in tensorflow/lite/ from the TensorFlow repository. Additionally, you will need header files from FlatBuffers and Abseil.