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In a machine learning one must separate the data in 3 sets, one for training, another for validation and another for tests with relative quantity to 70%, 15% and 15% respectively. However my doubt is regarding the actual amount of images/data for an ideal recognition training.
I have used opencv trainscascade to do the training, but unfortunately for my project I do not have a considerable number of both positive and negative images so I would need a minimum number for these two types of images, what would it be? And as for the stages, I am aware that the more stages are placed as the more specific the recognition becomes, which can generate even an excessive training, so the ideal number of stages is also around 10 and 20 ?
And finally, if I do a training generating a date Cascade.xml, and then do another that complements this first Cascade it is possible to attach them somehow without having to retrain everything again?
Below is an example of code to facilitate the visualization of the parameters for training:
opencv_traincascade -data 'diretório do cascade' \
-vec 'diretório das imagens POS.vec' \
-bg 'diretório das imagens NEG.txt' \
-numPos x \
-numNeg y \
-numStages z \
-w 24 -h 24 \
-mode ALL