ObjectDetection

Object Detection CLI application

Object Detection application for Windows on Snapdragon® with YOLO-X using ONNX runtime.

The app demonstrates how to use the QNN execution provider to accelerate the model using the Snapdragon® Neural Processing Unit (NPU).

Requirements

Platform

• Snapdragon® Platform (e.g. X Elite)
• Windows 11+

Tools and SDK

• Visual Studio 22
  • Download any variant of Visual Studio here
  • Make sure Desktop development with C++ tools are selected during installation or installed separately later

Build App

Downloading model from AI Hub

Download the float/onnx variant of YOLO-X from AI Hub. Rename it and place it into:

<project directory>/assets/models/detection.onnx

Build project in Visual Studio 22

1. Open ObjectDetection.sln

2. Setting up dependencies

   • NuGet packages

     • NuGet packages should automatically restore in Visual Studio during build
     • If packages are not restored automatically, try the following:
       • If prompted by Visual Studio to restore NuGet packages
         • Click on restore to restore all NuGet packages
       • Otherwise,
         • Go to Project -> Manage NuGet packages in Visual studio
         • Install ONNX-Runtime-QNN 1.19.0

   • vcpkg packages

     • Project is configured to work with vcpkg in manifest mode

     • If opencv headers are missing, vcpkg is not setup correctly.

     • Integrate vcpkg with Visual Studio:

       • Go to View -> Terminal in Visual studio
       • Run the following command in terminal

       vcpkg integrate install

3. Build project in Visual Studio

Running App

Running via Visual Studio

Visual studio project is configured with the following command arguments:

--model .\assets\models\detection.onnx --image .\assets\images\kitchen.jpg

You can simply run the app from Visual Studio to run object detection on sample image.

Running app via CLI

.\ARM64\Debug\ObjectDetection.exe --model .\assets\models\detection.onnx --image .\assets\images\kitchen.jpg

You can additionally run --help to get more information about all available options:

.\ARM64\Debug\ObjectDetection.exe --help

Please refer to QNN EP options that can be provided as --qnn_options to the app.

Sample Input

Sample Output

App and model details

1. Model input resolution: 640x640
   • If input image is of different shape, it's resized to 640x640
   • You can override model input dimensions if model uses different spatial image dimensions
2. App is built to work with post-processed outputs
   • App processes output logits and produces consumable output as follows:
     • Output boxes
     • Output scores
     • Output labels
   • If you want to try out any other model than Yolo (with post-processing included in model), please update output handling accordingly.

FAQ

1. If you get a DLL error message upon launch (for instance that opencv_core4d.dll was not found). Try Build -> Clean Solution and re-build. If this still happens, please go over the NuGet and vcpkg instructions again carefully.
2. How do I use a model with different input shape than 640x640?
   • Use --model_input_ht / --model_input_wt to model input dimensions.
3. I have a model that does not have post-processing embedded into the model. Can I still use the app?
   • You will have to modify the app and add the necessary post-processing to accommodate that models.

Project setup

Please see the Classification app for instructions of how to set up a project with ONNXRuntime QNN Execution Provider from scratch.