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In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architecturaldoi:10.1109/cvpr.2015.7299034 dblp:conf/cvpr/HosangOBS15 fatcat:cjcmsf7uwvcidor4ftr7gwxzsy