ML.Net ONNX Object Detection on ARM64 Raspberry PI

For the last month I have been working with preview releases of ML.Net with a focus on the Open Neural Network Exchange(ONNX) support. As part of my “day job” we have been running Object Detection models on X64 based industrial computers, but we are looking at moving to ARM64 as devices which support -20° to +60° operation appear to be easier to source.

The first step of my Proof of Concept(PoC) was to get the ONNX Object Detection sample working on a Raspberry Pi 4 running the 64bit version of Bullseye. I created a new solution which contained only the ONNX Object detection console application which would run on my desktop.

Initial Desktop Object Detection solution

To deploy applications I sometimes copy the contents of the “publish” directory to the device with WinSCP.

Object Detection application “publish to a folder configuration”

I also use Visual Studio Code with some scripts, or a modified version of Raspberry Debugger which supports deployment and debugging of applications to device running a 64 bit OS.

Object Detection console application release NuGet configuration

After updating my NuGet packages to the “release” versions the Object Detection console application would run on my desktop and process the sample images.

Object Detection console application running on my desktop

Getting the Object Detection console application running on my Raspberry Pi4 took a couple of attempts…

Debugging the Object Detection console application on my device

The first issue was the location of the sample images (changed assetsRelativePath)

using System;
using System.IO;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Drawing2D;
using System.Linq;
using Microsoft.ML;
using ObjectDetection.YoloParser;
using ObjectDetection.DataStructures;

namespace ObjectDetection
    class Program
      public static void Main()
            var assetsRelativePath = @"assets";
            DateTime startedAtUtc = DateTime.UtcNow;

            Console.WriteLine($"{startedAtUtc:yyyy:MM:dd HH:mm:ss} Start");

            string assetsPath = GetAbsolutePath(assetsRelativePath);
            var modelFilePath = Path.Combine(assetsPath, "Model", "TinyYolo2_model.onnx");
            var imagesFolder = Path.Combine(assetsPath, "images");
            var outputFolder = Path.Combine(assetsPath, "images", "output");

            // Initialize MLContext
            MLContext mlContext = new MLContext();

                // Load Data
                IEnumerable<ImageNetData> images = ImageNetData.ReadFromFile(imagesFolder);
                IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);

                // Create instance of model scorer
                var modelScorer = new OnnxModelScorer(imagesFolder, modelFilePath, mlContext);

                // Use model to score data
                IEnumerable<float[]> probabilities = modelScorer.Score(imageDataView);

                // Post-process model output
                YoloOutputParser parser = new YoloOutputParser();

                var boundingBoxes =
                    .Select(probability => parser.ParseOutputs(probability))
                    .Select(boxes => parser.FilterBoundingBoxes(boxes, 5, .5F));

                // Draw bounding boxes for detected objects in each of the images
                for (var i = 0; i < images.Count(); i++)
                    string imageFileName = images.ElementAt(i).Label;
                    IList<YoloBoundingBox> detectedObjects = boundingBoxes.ElementAt(i);

                    DrawBoundingBox(imagesFolder, outputFolder, imageFileName, detectedObjects);

                    LogDetectedObjects(imageFileName, detectedObjects);

            DateTime finishedAtUtc = DateTime.UtcNow;
            TimeSpan duration = finishedAtUtc - startedAtUtc;

            Console.WriteLine($"{finishedAtUtc:yyyy:MM:dd HH:mm:ss} Finish Duration:{duration.TotalMilliseconds}mSec");
         catch (Exception ex)

            Console.WriteLine("========= End of Process..Hit any Key ========");

        public static string GetAbsolutePath(string relativePath)
            FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
            string assemblyFolderPath = _dataRoot.Directory.FullName;

            string fullPath = Path.Combine(assemblyFolderPath, relativePath);

            return fullPath;

        private static void DrawBoundingBox(string inputImageLocation, string outputImageLocation, string imageName, IList<YoloBoundingBox> filteredBoundingBoxes)
            Image image = Image.FromFile(Path.Combine(inputImageLocation, imageName));

            var originalImageHeight = image.Height;
            var originalImageWidth = image.Width;

            foreach (var box in filteredBoundingBoxes)
                // Get Bounding Box Dimensions
                var x = (uint)Math.Max(box.Dimensions.X, 0);
                var y = (uint)Math.Max(box.Dimensions.Y, 0);
                var width = (uint)Math.Min(originalImageWidth - x, box.Dimensions.Width);
                var height = (uint)Math.Min(originalImageHeight - y, box.Dimensions.Height);

                // Resize To Image
                x = (uint)originalImageWidth * x / OnnxModelScorer.ImageNetSettings.imageWidth;
                y = (uint)originalImageHeight * y / OnnxModelScorer.ImageNetSettings.imageHeight;
                width = (uint)originalImageWidth * width / OnnxModelScorer.ImageNetSettings.imageWidth;
                height = (uint)originalImageHeight * height / OnnxModelScorer.ImageNetSettings.imageHeight;

                // Bounding Box Text
                string text = $"{box.Label} ({(box.Confidence * 100).ToString("0")}%)";

                using (Graphics thumbnailGraphic = Graphics.FromImage(image))
                    thumbnailGraphic.CompositingQuality = CompositingQuality.HighQuality;
                    thumbnailGraphic.SmoothingMode = SmoothingMode.HighQuality;
                    thumbnailGraphic.InterpolationMode = InterpolationMode.HighQualityBicubic;

                    // Define Text Options
                    Font drawFont = new Font("Arial", 12, FontStyle.Bold);
                    SizeF size = thumbnailGraphic.MeasureString(text, drawFont);
                    SolidBrush fontBrush = new SolidBrush(Color.Black);
                    Point atPoint = new Point((int)x, (int)y - (int)size.Height - 1);

                    // Define BoundingBox options
                    Pen pen = new Pen(box.BoxColor, 3.2f);
                    SolidBrush colorBrush = new SolidBrush(box.BoxColor);

                    // Draw text on image 
                    thumbnailGraphic.FillRectangle(colorBrush, (int)x, (int)(y - size.Height - 1), (int)size.Width, (int)size.Height);
                    thumbnailGraphic.DrawString(text, drawFont, fontBrush, atPoint);

                    // Draw bounding box on image
                    thumbnailGraphic.DrawRectangle(pen, x, y, width, height);

            if (!Directory.Exists(outputImageLocation))

            image.Save(Path.Combine(outputImageLocation, imageName));

        private static void LogDetectedObjects(string imageName, IList<YoloBoundingBox> boundingBoxes)
            Console.WriteLine($".....The objects in the image {imageName} are detected as below....");

            foreach (var box in boundingBoxes)
                Console.WriteLine($"{box.Label} and its Confidence score: {box.Confidence}");


The next issue was the location of the ONNX model on the device. I modified the properties of the TinyYolo2_model.onnx file so it was copied to the publish folder if it had been modified.

Visual Studio configured to copy TinyYolo2_model.onnx to the device

I then checked this was working as expected with WinSCP.

Confirming location of TinyYolo2_model.onnx on the device.

The platform specific runtime was missing so I confirmed the processor architecture with uname.

Using uname to get the processor architecture
Using WinSCP to copy the platform specified runtime to the application directory
Object Detection console application running on my Raspberry Pi4

I noticed that the Object Detection console application took significantly longer to run on the Raspberry PI4 so I added some code to display the duration.

Object Detection console application on my desktop
Object Detection application on the Raspberry PI

For my application I’m only interested in the Minimum Bounding Boxes(MBRs) so I disabled the code for drawing MBRs on the images.

Object Detection console application with no MBR drawing on my desktop
Object Detection console application with no MBR drawing on my Raspberry PI 4 device

Removing the code for drawing the MBRs on the images improved performance less than I was expecting.

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