How is acceptance ratio caclulated?
I've been working with Object Training for a while now, and I've found that generally cascades with an acceptance ratio of .0002 to .0007 are the most refined generally. However, I don't quite understand how acceptance ratio is calculated using traincascade. Could someone explain it to me or point me to a good reference that explains it?
Hmmm strange... i get best results when i go up to 10to the power -5...
Interesting, normally when mine are in that range the cascade picks up a lot of false positives, or sometimes nothing at all. How large are your sample sets generally?
it depends on the situation. Industrial cases about 1000 pos and 2500 neg. For more challenging cases about 5000 pos and 15000 neg.
Do you generate virtual samples? I mean, I am always afraid that generating new training samples from rotating, translating or maybe scaling relatively few basic samples doesn't provide various enough samples. I mean, imagine that the training learns artifacts from the sample? In other words, how do you generate your training samples without burning the cmos of a camera? ;)
No I never generate virtual samples. By far they introducing indeed artificial artefacts that are far from desired. I always use only positive samples of real life occurrences of the object, meaning I just burn CMOS time :)
Ok so I am not the only one :)