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I have read a fair amount about Haar training and I'm not clear on how many images one should use for the positive and negative sample sets. I see it recommended to use many images, some people recommend thousands. I'm also unclear about whether the number of positive and negative sample images should be the same?

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Here is the best tutorial on Haartraining: http://note.sonots.com/SciSoftware/haartraining.html

It says they used 5000 for positive and 3000 for negative.

This link says 3000 for positive and 5000 for negative. Anyway, a higher number of images improves the accuracy, but it also increases training time.

You can also refer to this https://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.htmllink for training your own OpenCV haar classifier:

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