I am trying to train a model that detects logos in documents. Since I am not really interested in what kind of logo there is, but simply if there is a logo, does it make sense to combine all logos into 1 logo class?
Or are "logos" too diverse to group them together (like some logos are round, some are rectangular, some are even text based etc.) and the diversity of features will just make it hard for the neural network to learn? Or doesn't it matter?
(I am currently trying out the YOLOv3 architecture to begin with. Any other suggestions better suited are also welcome)