  Deep learning architectures are showing great promise in various computer
vision domains including image classification, object detection, event
detection and action recognition. In this study, we investigate various aspects
of convolutional neural networks (CNNs) from the big data perspective. We
analyze recent studies and different network architectures both in terms of
running time and accuracy. We present extensive empirical information along
with best practices for big data practitioners. Using these best practices we
propose efficient fusion mechanisms both for single and multiple network
models. We present state-of-the art results on benchmark datasets while keeping
computational costs at a lower level. Another contribution of our paper is that
these state-of-the-art results can be reached without using extensive data
augmentation techniques.
