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.Categories:Building type (house, apartment building, condominium)Build year (old, new)Garage (boolean) - only for houseFloors (1, 2) - only for houseConstruction (standalone, row house) - only for houseMany long labels (softmax_classifier):house_old_nogarage_onefloor_standalonehouse_old_nogarage_onefloor_rowhouse_old_nogarage_twofloor_standalone...43 moreapartment_oldapartment_newFew short labels (something like a Multiclass Support Vector Machine):houseapartmentcondominiumnewold...7 moreAlternative to this would be to use a multi-label classifier by replacing the default softmax_classifier of the Inception Model v3 by something like the sequence_classifier.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?
Classifying multilabel images with TensorFlow
tensorflow;image classification;multilabel classification;image recognition;labels