I am trying to train a FOMO model. I have gone through all my images and labelled them. They are a mix of images where the object is present and labeled - and images with no labeled objects in them, but have things that are being incorrectly detected as the object. Are the images without objects being used in training? I remember that some Model training scripts only pull in images with objects.
I am trying to train a Bicyclist detector - but the model sometimes gets confused with cars (also have wheels) or people. Would it help if I also label those objects too? Would it help the model ignore them better?
yes all images are used; we add an implicit “background” class to all FOMO models so any area that you haven’t labelled with your objects of interest contribute towards the models understanding of the background. if you primary problem is false positives then you’re doing the right thing, more instances of the background will help.