How to use Tensorflow ONNX model in C# ML .NET for object detection

This is part 2 in the tutorial series on how to train a Tensorflow 2.x object detection model and use it in C# ML.NET. I advise you to read part 1 first if you haven’t already.

1. Introduction

I created a Visual Studio 2022 .NET 6.0 solution which contains all of the example code to get ML.NET object detection running. This code is based on a model which is specifically trained to detect SecurID RSA tokens in an image. These tokens look like this:

SecurID RSA token example

2. Clone GitHub repository with contains the full Visual Studio 2022 solution

You can clone the repository with the following command:

git clone

Once you have cloned the repository, open up the solution in Visual Studio 2022. You should now see the following directory structure:

Lanedirt.MLnet VS2022 solution structure

The solution contains 2 projects:

  • Lanedirt.MLnet
    • This project contains all of the C# ML.NET logic and a wrapper class which contains model-specific configuration and helper methods.
    • This project also contains an already trained TensorFlow model converted to .onnx (instructions on how to train your own TensorFlow 2.x ONNX model can be found in part #1 of this tutorial here). You can change this model to your own trained model by simply replacing the Model\rsa.onnx file.
  • Lanedirt.ModelTester
    • This is a simple WinForms GUI project where you can upload an image and see the object detection working in real life.

3. Run the Lanedirt.ModelTester project

Run the Lanedirt.ModelTester project in debug mode from Visual Studio 2022. You should then see the following screen:

Lanedirt.ModelTester GUI project

In this screen, click on the “Load image” button and select a .JPG file on your local computer which contains a RSA token.

Tip: for the sake of this example, the solution you just cloned contains 2 example images in the root of the project directory which you can use. For this example I will use the rsa_example_2.jpg file.

When the image is chosen, it will be feeded into the RSA .onnx model and the results will be parsed and shown on the screen. In the screenshot below you can see the original image, the annotated image, and the detected objects in JSON format.

ONNX ML.NET in action: detecting objects in the image we just uploade

4. Tips and tricks

Feel free to look at the code in this solution to see how the logic works behind the screens. The source code is well documented.

Everything in this solution is open-source, so you can change any part of the application to use in your own projects.

Any questions? Leave a comment and I will try to help you as good as I can. Good luck!

By Leendert de Borst

Freelance software architect with 10+ years of experience. Expert in translating complex technical problems into creative & simple solutions.

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