  Neural network designers have reached progressive accuracy by increasing
models depth, introducing new layer types and discovering new combinations of
layers. A common element in many architectures is the distribution of the
number of filters in each layer. Neural network models keep a pattern design of
increasing filters in deeper layers such as those in LeNet, VGG, ResNet,
MobileNet and even in automatic discovered architectures such as NASNet. It
remains unknown if this pyramidal distribution of filters is the best for
different tasks and constrains. In this work we present a series of
modifications in the distribution of filters in four popular neural network
models and their effects in accuracy and resource consumption. Results show
that by applying this approach, some models improve up to 8.9% in accuracy
showing reductions in parameters up to 54%.
