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Write a mobile object detection iOS application

Learn how to build an iOS object detection app with ONNX Runtime. This application continuously detect the objects in the frames seen by your iOS device’s back camera and display:

  • the class (type) of objects detected
  • the bounding box of the detected objects
  • the inference confidence

The application uses the pre-trained quantized MobileNet SSD V1 model.

This example is heavily based on Google Tensorflow lite - Object Detection Examples

Here’s an example screenshot of the app:

Screenshot showing iOS objection detection app

Contents

Pre-requisites

  • Xcode 12.5 and above (preferably latest version)
  • A valid Apple Developer ID
  • A real iOS device with a camera (preferably iphone 12/iphone 12 pro)
  • Xcode command line tools xcode-select --install
  • Clone the onnxruntime-inference-examples source code repo

Prepare the model for mobile deployment

  1. Create a separate Python environment so that this app’s dependencies are separate from other python projects

    conda create -n ios-app Python=3.8
conda activate ios-app

  2. Install Python dependencies

    cd <onnxruntime-inference-example-root>/mobile/examples/object_detection/ios/ORTObjectDetection
pip install -r ./prepare_model.requirements.txt

    The requirements file contains the onnxruntime, tf2onnx and tensorflow python packages required for model conversion in the next step.

  3. Download the model and convert it into ORT format

    ./prepare_model.sh

    The script:

    • downloads the original tflite model along with the model metadata labelmap.txt
    • converts it to ONNX format
    • further converts it to ORT format, which can be executed by the ONNX Mobile Runtime

    The script outputs a ModelsAndData directory, which contains the ORT format model, ssd_mobilenet_v1.all.ort and the model label data file, labelmap.txt.

    Conversion of this model is a two part process. The original model is in tflite format. This is firstly converted to ONNX format using the tf2onnx converter.

    The model is then converted into ORT format using the onnx to ort converter.

    As well as generating the model in ORT format, the conversion script also outputs an operator config file

Create iOS application

  1. Install CocoaPods

    sudo gem install cocoapods

  2. Install dependencies and generate workspace file

    cd <onnxruntime-inference-example-root>/mobile/examples/object_detection/ios/
pod install

    The Podfile contains the onnxruntime-mobile-objc dependency, which is the pod containing the Objective C API.

    At the end of this step, you should see a file called ORTObjectDetection.xcworkspace in the mobile/examples/object_detection/ios directory.

    This tutorial uses one of the pre-built packages for ONNX Runtime mobile. You can also build your own custom runtime if the demands of your target environment require it. To include the custom ONNX Runtime build in your iOS app, see Custom iOS package

  3. Build the project

    Open <onnxruntime-inference-example-root>/mobile/examples/object_detection/ios/ORTObjectDetection.xcworkspace in Xcode.

    Select your Development Team in the Signing & Capabilities workspace settings.

    Click on Product -> Build for Running to compile the application.

  4. Run the application

    Connect your iOS device and run the app. You’ll have to grant permissions for the app to use the device’s camera.

    You should see an app with the ONNX Runtime logo on your device. Running the app opens your camera and performs object detection. «Insert screenshot»