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One of the main problems with training a good classifier is that people take to less negative images. These images are used to model background clutter and variation, so your negative image set should be extremely large if you want to get a very robust classifier.
I managed to get a 96% detection rate by applying 2500 positives and 100.0000 negatives. I guess this makes my point.
