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TensorFlow Android Camera Demo

This folder contains an example application utilizing TensorFlow for Android devices.


The demos in this folder are designed to give straightforward samples of using TensorFlow in mobile applications.

Inference is done using the TensorFlow Android Inference Interface, which may be built separately if you want a standalone library to drop into your existing application.

Current samples:

  1. TF Classify: Uses the Google Inception model to classify camera frames in real-time, displaying the top results in an overlay on the camera image.
  2. TF Detect: Demonstrates a model based on Scalable Object Detection using Deep Neural Networks to localize and track people in the camera preview in real-time.
  3. TF Stylize: Uses a model based on A Learned Representation For Artistic Style to restyle the camera preview image to that of a number of different artists.

Prebuilt APK:

If you just want the fastest path to trying the demo, you may download the nightly build here. A device running Android 5.0 (API 21) or higher is required.

Running the Demo

Once the app is installed it can be started via the "TF Classify" and "TF Detect" and icons, which have the orange TensorFlow logo as their icon.

While running the activities, pressing the volume keys on your device will toggle debug visualizations on/off, rendering additional info to the screen that may be useful for development purposes.

Building the Demo from Source

Pick your preferred approach below. At the moment, we have full support for Bazel, and partial support for gradle, cmake, make, and Android Studio.

As a first step for all build types, clone the TensorFlow repo with:

git clone --recurse-submodules

Note that --recurse-submodules is necessary to prevent some issues with protobuf compilation.


Install Bazel and Android Prerequisites

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

  1. Get the recommended Bazel version listed in os_setup.html
  2. The Android NDK is required to build the native (C/C++) TensorFlow code. The current recommended version is 12b, 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 required to build the TF Android demo.

The Android entries in <workspace_root>/WORKSPACE must be uncommented with the paths filled in appropriately depending on where you installed the NDK and SDK. Otherwise an error such as: "The external label '//external:android/sdk' is not bound to anything" will be reported.

Also edit the API levels for the SDK in WORKSPACE to the highest level you have installed in your SDK. This must be >= 23 (this is completely independent of the API level of the demo, which is defined in AndroidManifest.xml). The NDK API level may remain at 21.

Install Model Files (optional)

The TensorFlow GraphDefs that contain the model definitions and weights are not packaged in the repo because of their size. They are downloaded automatically and packaged with the APK by Bazel via a new_http_archive defined in WORKSPACE during the build process.

Optional: If you wish to place the models in your assets manually (E.g. for non-Bazel builds), remove the inception_5 and mobile_multibox entries in BUILD and download the archives yourself to the assets directory in the source tree:

$ curl -L -o /tmp/
$ curl -L -o /tmp/

$ unzip /tmp/ -d tensorflow/examples/android/assets/
$ unzip /tmp/ -d tensorflow/examples/android/assets/

This will extract the models and their associated metadata files to the local assets/ directory.


After editing your WORKSPACE file to update the SDK/NDK configuration, you may build the APK. Run this from your workspace root:

$ bazel build -c opt //tensorflow/examples/android:tensorflow_demo

If you get build errors about protocol buffers, run git submodule update --init and make sure that you've modified your WORKSPACE file as instructed, then try building again.


Make sure that adb debugging is enabled on your Android 5.0 (API 21) or later device, then after building use the following command from your workspace root to install the APK:

$ adb install -r bazel-bin/tensorflow/examples/android/tensorflow_demo.apk

Android Studio

Android Studio may be used to build the demo in conjunction with Bazel. First, make sure that you can build with Bazel following the above directions. Then, look at build.gradle and make sure that the path to Bazel matches that of your system.

At this point you can add the tensorflow/examples/android directory as a new Android Studio project. Click through installing all the Gradle extensions it requests, and you should be able to have Android Studio build the demo like any other application (it will call out to Bazel to build the native code with the NDK).


Full CMake support for the demo is coming soon, but for now it is possible to build the TensorFlow Android Inference library using tensorflow/contrib/android/cmake.