Write a mobile image classification Android application

This app uses image classification to continuously classify the objects it sees from the device’s camera in real-time and displays the most probable inference results on the screen.

This example is loosely based on Google CodeLabs - Getting Started with CameraX

The pre-trained TorchVision MOBILENET V2 is used in this sample app.



Prepare the model and data used in the application

  1. Convert the model to ORT format

    Open Mobilenet v2 Quantization with ONNX Runtime Notebook, this notebook will demonstrate how to:

    • Export the pre-trained MobileNet V2 FP32 model from PyTorch to a FP32 ONNX model
    • Quantize the FP32 ONNX model to an uint8 ONNX model
    • Convert both FP32 and uint8 ONNX models to ORT models

    Note: this step is optional, you can download the FP32 and uint8 ORT models here.

  2. Download the model class labels

    wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
  3. Copy the labels file and models into the sample application resource directory

    cd onnxrutime-inference-examples
    cp *.ort mobile/examples/image_classification/android/app/src/main/res/raw/
    cp imagenet_classes.txt mobile/examples/image_classification/android/app/src/main/res/raw/

As an alternative to steps 1-3, you can use this pre-built script to download the models and data to the correct directory:

cd onnxruntime-inference-examples

Create the Android application

  1. Open the sample application in Android Studio

    Open Android Studio and select Open an existing project, browse folders and open the folder mobile/examples/image_classification/android/.

    Screenshot showing Android Studio Open an Existing Project

    This project uses the published Android package for ONNX Runtime. You can also customize ONNX Runtime to reduce the size of the application by only including the operators from the model. For more information on how to do this, and how to include the resulting package in your Android application, see the custom build instruction for Android

  2. Build the application

    Select Build -> Make Project in the top toolbar in Android Studio and check the projects has built successfully.

    Screenshot showing Android Studio build command

    Screenshot showing successful build in Android Studio

  3. Connect your android device and run the app

    Connect your Android Device to the computer and select your device in the top-down device bar.

    Screenshot showing connection to device in Android Studio

    Then Select Run -> Run app and this will prompt the app to be installed on your device.

    Now you can test and try by opening the app ort_image_classifier on your device. The app may request your permission to use the camera.

    Here’s an example screenshot of the app.

    Screenshot showing an example classification of a toy terrier dog