Updated: 5 July 2023

This page will discuss the additional steps to follow to build an ATAK plugin capable of running a trained TensorFlow (TF) Lite model locally on you Android device. Please ensure you have a plugin ready for these modification by completing the “Creating your own Plugin: First Steps” before you proceed with the following instructions.

1. Gradle Change: Protect Model

Within the android block of the app/build.gradle, add the Android Asset Packaging Tool (AAPT) Options aaptOptions with the noCompress "tflite" option to ensure your plugin’s *.tflite model files are not compressed during the build process. See Below.

2. Gradle Change: Enable ML Model Binding

Within the android block of the app/build.gradle, add the Android Build Features buildFeatures with the Android Machine Learning (ML) Model Binding feature buildFeatures option enabled. See Below.

  android {
	...
    aaptOptions {
        noCompress "tflite"
    }
	buildFeatures {
    	mlModelBinding true
	}
}

3. Import a TensorFlow Lite Model

Import a TF Lite Model (*.tflite) by right clicking the main directory (app/main/) and selecting New -> Other -> Tensorflow Lite Model. In the pop up, specify the path to your desired model.

This will create a new folder main/ml in your project with the imported TF Lite model inside. Additionally, the following dependencies will be added to the app/build.gradle file if this is the first model you have imported:

  dependencies {
    ...
	implementation 'org.tensorflow:tensorflow-lite-support:0.1.0'
	implementation 'org.tensorflow:tensorflow-lite-metadata:0.1.0'
	implementation 'org.tensorflow:tensorflow-lite-gpu:2.3.0'
}

We recommend increasing the tensorflow-lite-support to 0.4.3 to make use of newer features:

  implementation 'org.tensorflow:tensorflow-lite-support:0.4.3'

4. Access Your Model

To check that everything was setup correctly you should be able to build “Make Project” successfully. To access and run your model, read the next section.