iOS Demo App

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. These instructions walk you through building and running the demo on an iOS device.


  • You must have Xcode installed and have a valid Apple Developer ID, and have an iOS device set up and linked to your developer account with all of the appropriate certificates. For these instructions, we assume that you have already been able to build and deploy an app to an iOS device with your current developer environment.

  • The demo app requires a camera and must be executed on a real iOS device. You can build it and run with the iPhone Simulator but it won't have any camera information to classify.

  • You don't need to build the entire TensorFlow library to run the demo, but you will need to clone the TensorFlow repository if you haven't already:

    git clone
  • You'll also need the Xcode command-line tools:

    xcode-select --install

    If this is a new install, you will need to run the Xcode application once to agree to the license before continuing.

Building the iOS Demo App

  1. Install CocoaPods if you don't have it:

    sudo gem install cocoapods
  2. Download the model files used by the demo app (this is done from inside the cloned directory):

    sh tensorflow/lite/examples/ios/
  3. Install the pod to generate the workspace file:

    cd tensorflow/lite/examples/ios/camera
    pod install

    If you have installed this pod before and that command doesn't work, try

    pod update

    At the end of this step you should have a file called tflite_camera_example.xcworkspace.

  4. Open the project in Xcode by typing this on the command line:

    open tflite_camera_example.xcworkspace

    This launches Xcode if it isn't open already and opens the tflite_camera_example project.

  5. Build and run the app in Xcode.

    Note that as mentioned earlier, you must already have a device set up and linked to your Apple Developer account in order to deploy the app on a device.

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!