Best practices to train deep models on imbalanced datasets—a case study on animal detection in aerial imagery

Benjamin Kellenberger*, Diego Marcos, Devis Tuia

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

We introduce recommendations to train a Convolutional Neural Network for grid-based detection on a dataset that has a substantial class imbalance. These include curriculum learning, hard negative mining, a special border class, and more. We evaluate the recommendations on the problem of animal detection in aerial images, where we obtain an increase in precision from 9% to 40% at high recalls, compared to state-of-the-art. Data related to this paper are available at: http://doi.org/10.5281/zenodo.609023.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
EditorsUlf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
PublisherSpringer Verlag
Pages630-634
ISBN (Print)9783030109967
DOIs
Publication statusPublished - 1 Jan 2019
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland
Duration: 10 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11053 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
CountryIreland
CityDublin
Period10/09/1814/09/18

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Keywords

  • Class imbalance
  • Deep learning
  • Unmanned Aerial Vehicles

Cite this

Kellenberger, B., Marcos, D., & Tuia, D. (2019). Best practices to train deep models on imbalanced datasets—a case study on animal detection in aerial imagery. In U. Brefeld, A. Marascu, F. Pinelli, E. Curry, B. MacNamee, N. Hurley, E. Daly, ... M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings (pp. 630-634). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_40