Deep Residual Networks for Plankton Classification

被引：26

作者：

Li, Xiu ^{[1
,2
]}

Cui, Zuoying ^{[1
,2
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China

来源：

OCEANS 2016 MTS/IEEE MONTEREY | 2016年

关键词：

plankton classification; residual networks; Deep learning;

D O I：

10.1109/OCEANS.2016.7761223

中图分类号：

P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

In this paper, we introduce a deep residual network to classify images of plankton. The Plankton Dataset, which consists of 30,336 plankton images of 121 classes, was used for a data science competition hosted on the Kaggle platform(1). We finally achieved a top-5 accuracy of 95.8% and a nearly real-frame rate of 9.1ftps, which is close to the accuracy of the No. 1 team ( over 98%, 1.4ftps) (2) in the competition but much faster than their classification on a single image.

引用

页数：4