In another topic of the forum, I asked information about the best practices in the annotation work.
My question was:
“In terms of best practices related to the annotation work, with the focus to optimise the ML training, should I make as many labels as there are variations of one sort of object (i.e. analogical clock, digital clock, Grandfather clock…) or is it fine for the ML training to make just one label to cover all the variations of the object. (i.e. Clock)”
treebeard kindly replied:
" What we saw often is that this is where attributes are very useful.
You want to be careful that you don’t unfairly penalise the neural network (NN) and also that there is good representation for different events.
If you make a different label class for every brand of Sedan car then the NN will get penalised when guessing BMW vs Mercedes and waste a lot of energy trying to resolve that.
There it makes more sense to have class as “sedan” and assign an attribute as brand that can focus a different model entirely on trying to make that distinction.
To your example, it might make sense to have class a clock and then attributes as analog, digital etc."
Let’s imagine that the annotation work is completed, I have labels of clocks containing attributes to describe the various types of clock : “analogic, digital, grandfather_clock”
My question to you is this one: are you aware of a method (via opencv, python, yolov3, other?) to retrieve the type attribute of the clock that you identify in a picture that you are scanning.
(my current work environment is Python 3.7 & Opencv to open,read,identify objects in a picture)
Up to now, I was unfortunate in my various investigations on Internet.
Thanks for the attention you’ll grant to my question!