A More Efficient CNN Architecture for Plankton Classification

Jiangpeng Yan, Xiu Li, Zuoying Cui

Abstract: With mass data collected by seafloor observation networks, an autonomous system which helps to annotate these pictures are in great demand. In this paper, we study the relationship between the network architecture and the classification accuracy for the Plankton Dataset collected by Oregon State University’s Hatfield Marine Science Center. We use multiple classic deep convolutional neural networks (CNN) models to compare the benefit and cost of deeper models which have performed quite well in ImageNet Large Scale Visual Recognition Challenge (ILSVRC) ( http://www.image-net.org/challenges/LSVRC ) competitions and we discover a hidden degeneration phenomenon. Then we conclude some skills to make CNN smaller and finally propose a more efficient network architecture whose model is much smaller (only 1.5 MB), faster (32.2 fps) and achieve a top-5 accuracy of 96% in the Plankton Dataset. This model can be actually deployed in the seafloor observation network system with its advantages.