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nneg is the amount of negative windows, that are grabbed from your set of negative images. It are not the images itself, but the negative windows, equalling the size of your model window that are randomly grabbed from your negatives dataset. So yes, you can actually use like 3 images of 1000x1000 pixels and deduce over 1000 negative windows of 15x15 pixels for example.

Your second remark is correct. We want the general generated error on your detections to drop under the false acceptance rate to the power the amount of stages. If this happens training can be stopped early (extra stopping criteria) because your model reaches the required quality with less stages.

0.995 Det rate means that you want to 99.5% of all objects that are actually in your positive dataset by the trained classifier at each stage (existing of a combination of weak classifiers of each single stage).

The negatives are used to check if negatives get wrongly classified! And thus generating error :)

Hope this helps out!