Android quickstart

To get started with TensorFlow Lite on Android, we recommend exploring the following example.

Android image classification example

Read TensorFlow Lite Android image classification for an explanation of the source code.

This example app uses image classification to continuously classify whatever it sees from the device's rear-facing camera. The application can run either on device or emulator.

Inference is performed using the TensorFlow Lite Java API and the TensorFlow Lite Android Support Library. The demo app classifies frames in real-time, displaying the top most probable classifications. It allows the user to choose between a floating point or quantized model, select the thread count, and decide whether to run on CPU, GPU, or via NNAPI.

Build in Android Studio

To build the example in Android Studio, follow the instructions in

Create your own Android app

To get started quickly writing your own Android code, we recommend using our Android image classification example as a starting point.

The following sections contain some useful information for working with TensorFlow Lite on Android.

Use the TensorFlow Lite Android 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.

To get started, follow the instructions in the TensorFlow Lite Android Support Library

Use the TensorFlow Lite AAR from JCenter

To use TensorFlow Lite in your Android app, we recommend using the TensorFlow Lite AAR hosted at JCenter.

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

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly'

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.

We recommend most developers omit the x86, x86_64, and arm32 ABIs. This can be achieved with the following Gradle configuration, which specifically includes only armeabi-v7a and arm64-v8a, which should cover most modern Android devices.

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

To learn more about abiFilters, see NdkOptions in the Android Gradle documentation.

Build TensorFlow Lite locally

In some cases, you might wish to use a local build of TensorFlow Lite. For example, you may be building a custom binary that includes operations selected from TensorFlow, or you may wish to make local changes to TensorFlow Lite.

Set up build environment using Docker

  • Download the Docker file. By downloading the Docker file, you agree that the following terms of service govern your use thereof:

By clicking to accept, you hereby agree that all use of the Android Studio and Android Native Development Kit will be governed by the Android Software Development Kit License Agreement available at (such URL may be updated or changed by Google from time to time).

You must acknowledge the terms of service to download the file. Acknowledge

  • You can optionally change the Android SDK or NDK version. Put the downloaded Docker file in an empty folder and build your docker image by running:
docker build . -t tflite-builder -f tflite-android.Dockerfile
  • Start the docker container interactively by mounting your current folder to /tmp inside the container (note that /tensorflow_src is the TensorFlow repository inside the container):
docker run -it -v $PWD:/tmp tflite-builder bash

If you use PowerShell on Windows, replace "$PWD" with "pwd".

If you would like to use a TensorFlow repository on the host, mount that host directory instead (-v hostDir:/tmp).

  • Once you are inside the container, you can run the following to download additional Android tools and libraries (note that you may need to accept the license):
android update sdk --no-ui -a --filter tools,platform-tools,android-${ANDROID_API_LEVEL},build-tools-${ANDROID_BUILD_TOOLS_VERSION}

You can now proceed to the "Build and Install" section. After you are finished building the libraries, you can copy them to /tmp inside the container so that you can access them on the host.

Set up build environment without Docker

Install Bazel and Android Prerequisites

Bazel is the primary build system for TensorFlow. To build with it, you must have it and the Android NDK and SDK installed on your system.

  1. Install the latest version of the Bazel build system.
  2. The Android NDK is required to build the native (C/C++) TensorFlow Lite code. The current recommended version is 17c, which may be found here.
  3. The Android SDK and build tools may be obtained here, or alternatively as part of Android Studio. Build tools API >= 23 is the recommended version for building TensorFlow Lite.
Configure WORKSPACE and .bazelrc

Run the ./configure script in the root TensorFlow checkout directory, and answer "Yes" when the script asks to interactively configure the ./WORKSPACE for Android builds. The script will attempt to configure settings using the following environment variables:


If these variables aren't set, they must be provided interactively in the script prompt. Successful configuration should yield entries similar to the following in the .tf_configure.bazelrc file in the root folder:

build --action_env ANDROID_NDK_HOME="/usr/local/android/android-ndk-r17c"
build --action_env ANDROID_NDK_API_LEVEL="21"
build --action_env ANDROID_BUILD_TOOLS_VERSION="28.0.3"
build --action_env ANDROID_SDK_API_LEVEL="23"
build --action_env ANDROID_SDK_HOME="/usr/local/android/android-sdk-linux"

Build and install

Once Bazel is properly configured, you can build the TensorFlow Lite AAR from the root checkout directory as follows:

bazel build -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
  --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \

This will generate an AAR file in bazel-bin/tensorflow/lite/java/. Note that this builds a "fat" AAR with several different architectures; if you don't need all of them, use the subset appropriate for your deployment environment. From there, there are several approaches you can take to use the .aar in your Android Studio project.

Add AAR directly to project

Move the tensorflow-lite.aar file into a directory called libs in your project. Modify your app's build.gradle file to reference the new directory and replace the existing TensorFlow Lite dependency with the new local library, e.g.:

allprojects {
    repositories {
        flatDir {
            dirs 'libs'

dependencies {
    compile(name:'tensorflow-lite', ext:'aar')
Install AAR to local Maven repository

Execute the following command from your root checkout directory:

mvn install:install-file \
  -Dfile=bazel-bin/tensorflow/lite/java/tensorflow-lite.aar \
  -DgroupId=org.tensorflow \
  -DartifactId=tensorflow-lite -Dversion=0.1.100 -Dpackaging=aar

In your app's build.gradle, ensure you have the mavenLocal() dependency and replace the standard TensorFlow Lite dependency with the one that has support for select TensorFlow ops:

allprojects {
    repositories {

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:0.1.100'

Note that the 0.1.100 version here is purely for the sake of testing/development. With the local AAR installed, you can use the standard TensorFlow Lite Java inference APIs in your app code.

Build Android app using C++

There are two ways to use TFLite through C++ if you build your app with the NDK:

Use TFLite C API

This is the recommended approach. Download the TensorFlow Lite AAR hosted at JCenter, rename it to tensorflow-lite-*.zip, and unzip it. You must include the four header files in headers/tensorflow/lite/ and headers/tensorflow/lite/c/ folder and the relevant dynamic library in jni/ folder in your NDK project.

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

Use 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.