The TensorFlow Lite demo is a camera app that continuously classifies whatever it sees from your device's back camera, using a quantized MobileNet model.
You'll need an Android device running Android 5.0 or higher to run the demo.
To get you started working with TensorFlow Lite on Android, we'll walk you through building and deploying our TensorFlow demo app in Android Studio.
It's also possible to build the demo app with Bazel, but we only recommend this for advanced users who are very familiar with the Bazel build environment. For more information on that, see our page on Github.
Build and deploy with Android Studio
Clone the TensorFlow repository from GitHub if you haven't already:
git clone https://github.com/tensorflow/tensorflow
Install the latest version of Android Studio from here.
From the Welcome to Android Studio screen, use the Import Project (Gradle, Eclipse ADT, etc) option to import the
tensorflow/contrib/lite/java/demodirectory as an existing Android Studio Project.
Android Studio may prompt you to install Gradle upgrades and other tool versions; you should accept these upgrades.
Download the TensorFlow Lite MobileNet model from here.
Unzip this and copy the
mobilenet_quant_v1_224.tflitefile to the assets directory:
Build and run the app in Android Studio.
You'll have to grant permissions for the app to use the device's camera. Point the camera at various objects and enjoy seeing how the model classifies things!