Many authors in the computer vision community use the entire training dataset to validate or test their methods... my question is what is the best metric/parameters that can be used to measure a good training set? and I wonder if any of you can briefly describe what do we mean by a good training set? From my perspectives, I would say a good training set is the one that leverage the performance of the detectors or classifier while possessing the generalization capabilities.