Hi, Both approaches use the same kind of scaling. However, as a test setup I stored an image on disk with the exact dimensions (width, height) as expected by my CoreML model. So normally there is no need for any scaling, but again to be sure I have added the scaleFit scaling option to both approaches. So both approaches should have the exact same input image, but still the difference in confidence still occurs... What happens internally to get/calculate the confidence of a VNRecognizedObjectObservation? In the documentation it only says it is a normalised value. For the boundingBox it is clear that it is "recalculated" to another coordinate origin, however it is unclear to me what happens internally with the confidence score... Thanks!

Hi @gchiste I believe the above explanation is out-of-date. Could you explain what how the new implementation works? According to the documentation of VNRecognizedObjectObservation you have to Multiply the classification confidence with the confidence of this observation. but how does Vision acquire these different confidences (both for the object observation as for the classification). The underlying CoreML detection model (e.g. YOLOv3-Tiny) has only one output that generates confidences so I am wondering how Vision is able to decouple this confidence into two different ones. Is it also possible to give me an idea of when this implementation changed? Thanks!