The dataset that I am categorizing with TensorFlow ML library contains multiple labels per image. The contents are real estate images photographed from outside that are analyzed for various image features.
The question is how to assign the labels, the most straightforward way is to use either many long labels, or few short labels.
Building type ("house", "apartment building", "condominium") Build year ("old", "new") Garage (boolean) - only for house Floors (1, 2) - only for house Construction ("standalone", "row house") - only for house
Many long labels (
house_old_nogarage_onefloor_standalone house_old_nogarage_onefloor_row house_old_nogarage_twofloor_standalone ...43 more apartment_old apartment_new
Few short labels (something like a
Multiclass Support Vector Machine):
house apartment condominium new old ...7 more
Alternative to this would be to use a multi-label classifier by replacing the default
Inception Model v3by something like the
I want to represent the data accurately, but also I don't mind doing simple categorization if the accuracy is acceptable.
Which of the proposed 3 labeling solutions suits the problem?