a few precisions on the FP_classifier model?

[kho-bluefrogrobotics]

Hello,

I tried to use your provided human clasifier model (greatly apreciated, btw, Thanks! )

However, I obtain weird and unstable results.

Could you give some insights on how to use this model?

I guessed that the first output is for the background recognition, and the second for the humans, right?
What are the expected output values?
I guess we need to normalise the input image, with what mean and std_var values?
Does it work with humans that are seated, cropped, horizontal?

Thanks for your answer!

PS: I'm using the OpenCV dnn engine
which looks like smthing like that (Java):

             Net humanClassifier;
             humanClassifier = Dnn.readNet("./FP_classifier.onnx");
             Mat blob = Dnn.blobFromImage(inputImage, 1/255,
                    new org.opencv.core.Size(32, 56),
                    new Scalar(new double[]{0.0, 0.0, 0.0}), /*swapRB*/false, /*crop*/false, CV_32F);
            humanClassifier.setInput(blob);

            List<Mat> outputs = new ArrayList<Mat>();
            humanClassifier.forward(outputs);