The goal of this thesis is to describe and use method Faster R-CNN for detection and recognition of traffic signs. It explores the possibility of using artificially generated images in validation set, in hopes of saving real images for train set. We tackle a real world problem of growing dataset through time. We'll try to find an optimal way to augment the already learned model with new images. Lastly, we try to apply a new method, online hard example mining, which is essentially bootstrapping for end-to-end systems.