|Title||Best practices to train deep models on imbalanced datasets—a case study on animal detection in aerial imagery|
|Author(s)||Kellenberger, Benjamin; Marcos, Diego; Tuia, Devis|
|Source||In: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. - Springer Verlag (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ) - ISBN 9783030109967 - p. 630 - 634.|
|Event||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018, Dublin, 2018-09-10/2018-09-14|
Laboratory of Geo-information Science and Remote Sensing
|Publication type||Contribution in proceedings|
|Keyword(s)||Class imbalance - Deep learning - Unmanned Aerial Vehicles|
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.