ML.NET object detection Predict() takes 5 seconds to process

[MattiaDurli]

System Information (please complete the following information):

• OS & Version: Windows 10
• ML.NET Version: ML.NET v1.6
• .NET Version: NET 5.0

Describe the bug I've created a ONNX model for Object Detection with Visual Studio and ML Model Builder (using an Azure workspace), using VOTT to define the 4 objects I want to detect. I'm testing the model as explained in the tutorial, and it works well, detects the 4 objects, result is ok:

    var sampleData = new MLModel1.ModelInput()
    {
        ImageSource = @"C:\Data\sample1.jpg",
    };

    //Load model and predict output
    var result = MLModel1.Predict(sampleData);

Problem is it takes 5 seconds (10 seconds on first run, 5 on the following ones). Sample.jpg is a 700x400 pixels image, 85kb, the computer is an Intel i7 2.9GHz.

Am I doing something wrong or this is the speed I should expect? Here's the image, the objects to detect are the REF, LOT, the hourglass icon and the factory icon.

To Reproduce Followed the Object Detection sample on Documentation

Expected behavior Much faster processing time

[michaelgsharp]

@JakeRadMSFT is this normally the times you see from modelbuilder for this kind of stuff?

@MattiaDurli Can you share the pipeline code and model you are using?

[MattiaDurli]

Yes, this is the pipeline created by model builder:

IEstimator<ITransformer> pipeline =  mlContext.Transforms.LoadImages(@"input", @"ImageSource")
                                    .Append(mlContext.Transforms.ResizeImages(imageWidth: 800, imageHeight: 600, outputColumnName: @"input", inputColumnName: @"input", cropAnchor: ImageResizingEstimator.Anchor.Center, resizing: ImageResizingEstimator.ResizingKind.Fill))
                                    .Append(mlContext.Transforms.ExtractPixels(outputColumnName: @"input", inputColumnName: @"input", colorsToExtract: ImagePixelExtractingEstimator.ColorBits.Rgb, orderOfExtraction: ImagePixelExtractingEstimator.ColorsOrder.ARGB))
                                    .Append(mlContext.Transforms.ApplyOnnxModel(modelFile: @"C:\Data\Code\Projects\CTServerCV\CTServerCV\LabelDetection\MLModel1.onnx"));

Here's the model created: https://www.dropbox.com/s/370drmd1yuyg5kv/MLModel1.zip?dl=0

I really haven't done anything, just followed the default for Object Detection with AutoML I edited the consumption sample created by AutoML and measured now the Predict function only (in my previous benchmark I calculated also the MLContext creation), and time dropped from 5s to 2.5s, but still seems a lot to me.

[lovettchris]

You might try separating the loading of the engine from the prediction time, like this:

var engine = ConsumeModel.CreatePredictionEngine();
stopwatch.Start();
ModelOutput result = engine.Predict(input);

or do the prediction multiple times to count out the first prediction time and see what the average is after that...

[MattiaDurli]

Yes I tried that, and time dropped from 5s to 2.5s but still seems a lot to me. I try to locate only 4 different objects, and I've seen demos with other frameworks where the object recognition is at multiple times per second, that's why I suspect something's wrong.

[michaelgsharp]

Hey @MattiaDurli, can you upload your model and OnnxModel with 1 sample and code to run it? I have an idea I want to play around with. If you are still looking into this anyways.