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I would consider making the ratio of positive and negatives 1:2. So if you have 8188 negative take approximately 4000 positive samples. Also you should consider this if you don't want your traincascade crashes. Some already used positive samples can be filtered by each previous stage (i.e. recognized as background) so if you put maximum of your positive sample and traincascade rejects some of them it can cause a crash. And also i think you have a lot of background in your positive samples, you should consider cropping the flowers from some of them. But it's just my opinion :)
