What kind of architecture to use for non-binary output multi-label image clasification

I want to make a network for making multi-label attribute classifications on images of clothing. This is a simplified case of what I want to do, I have 9 different attribute categories that I wish to detect on the image of a T-shirt: Color, Arm Type, Collar type, Pattern, Fabric Type, Size, Usage Area, and Style. However, you may notice that these attributes are non-binary, for example, color attribute of the clothing can take 18 different values or the collar type can take 11 different values like a round collar, bicycle collar, turtleneck, etc. Most tutorials I could find on the internet are mostly for binary labels like is the picture of a celebrity is bald or not, blonde or not. It is all 1 or 0. Another important point is that not every attribute has a value for every entry in my dataset. I am planning to train this on a dataset that I custom-made myself and there are different lines of clothing than just t-shirts. For example, in the data labeling files of my dataset, there is not a "collar type" specified for a picture of pants because pants obviously do not even have a collar. So I want to train a multi-label classification model with 9 different outputs with images in a database that has 3-4 attributes labeled each. I am an engineering student who is just getting in on machine learning and AI so it would be awesome if you can include similar tutorials, articles etc in your answers so I can self-teach myself how to do it. Thank you so much Note: I am adding an excel…