This is an application for TensorFlow Lite on Android. It uses Image classification to continuously classify whatever it sees from the device's back camera. Inference is performed using the TensorFlow Lite Java API. The app classifies frames in real-time, displaying the top most probable classifications. It allows the user to choose between a different optimized models (floating point or quantized) model, select the thread count, and decide whether to run on CPU, GPU, or via NNAPI.

These instructions walk you through building and running the app on an Android device.

We provide several models bundled in this App:

• MobileNetV2 (float32, float16, integer quantized)
• InceptionV3 (float32, float16, integer quantized)
• NASNetMobile (float32, float16, integer quantized)

For details of the models used and optimizations performed on it, visit this repository.

Downloading, extracting, and placing the models in the assets folder is managed automatically by download.gradle.

• Android Studio 3.6.1

• Android device in developer mode with USB debugging enabled

• USB cable (to connect Android device to your computer)

Clone the GitHub repository to your computer to get the application.

git clone https://github.com/Fedeee9/rubbish_detection_app

Open the project in Android Studio. To do this, open Android Studio and select Open an existing project, setting the folder to rubbish_detection_app/android

Select Build -> Make Project and check that the project builds successfully. You will need Android SDK configured in the settings. You'll need at least SDK version 28. The build.gradle file will prompt you to download any missing libraries.

The file download.gradle directs gradle to download the two models used in the example, placing them into assets.

`build.gradle` is configured to use TensorFlow Lite's nightly build.

If you see a build error related to compatibility with Tensorflow Lite's Java API (for example, `method X is undefined for type Interpreter`), there has likely been a backwards compatible change to the API. You will need to run `git pull` in the examples repo to obtain a version that is compatible with the nightly build.

Connect the Android device to the computer and be sure to approve any ADB permission prompts that appear on your phone. Select Run -> Run app. Select the deployment target in the connected devices to the device on which the app will be installed. This will install the app on the device.

To test the app, open the app called Rubbish Detector on your device. When you run the app the first time, the app will request permission to access the camera.

• Luca Antognetti
• Federico Dal Monte