Plant identification with large number of species: SabanciU-gebzeTU system in plantCLEF 2017

Abstract

We describe the plant identification system that was submitted to the LifeCLEF plant identification campaign in 2017 [1], as a collaboration of Sabanci University and Gebze Technical University in Turkey. Similar to our system that got a very close second place in 2016, we fine-tuned two well-known deep learning architectures (VGGNet and GoogLeNet) that were pre-trained on the object recognition dataset of ILSVRC 2012 and used an ensemble of 4-9 networks using score-level combination for the submitted systems. Our best system was obtained with a classifier fusion of 9 networks trained with some differences in training (network architecture, data, or initialization), achieving an average inverse rank of 0.634 on the official test data, while the first place system achieved an impressive score of 0.92. © 2017 Elsevier B.V., All rights reserved.