A Computationally Efficient Neural Network For Faster Image Classification

Abstract

Deep Convolutional Neural Networks have led to series of breakthroughs in image classification. With increasing demand to run DCNN based models on mobile platforms with minimal computing capabilities and lesser storage space, the challenge is optimizing those DCNN models for lesser computation and smaller memory footprint. This paper presents a highly efficient and modularized Deep Neural Network (DNN) model for image classification, which outperforms state of the art models in terms of both speed and accuracy. The proposed DNN model is constructed by repeating a building block that aggregates a set of transformations with the same topology. In order to make a lighter model, it uses Depthwise Separable convolution, Grouped convolution and identity shortcut connections. It reduces computations approximately by 100M FLOPs in comparison to MobileNet with a slight improvement in accuracy when validated on CIFAR-10, CIFAR-100 and Caltech-256 datasets.