Ensembles of Vision Transformers as a New Paradigm for Automated
Classification in Ecology
- URL: http://arxiv.org/abs/2203.01726v1
- Date: Thu, 3 Mar 2022 14:16:22 GMT
- Title: Ensembles of Vision Transformers as a New Paradigm for Automated
Classification in Ecology
- Authors: S. Kyathanahally, T. Hardeman, M. Reyes, E. Merz, T. Bulas, F. Pomati,
and M. Baity-Jesi
- Abstract summary: We show that ensembles of Data-efficient image Transformers (DeiTs) significantly outperform the previous state of the art (SOTA)
On all the data sets we test, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 18.48% to 87.50%.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Monitoring biodiversity is paramount to manage and protect natural resources,
particularly in times of global change. Collecting images of organisms over
large temporal or spatial scales is a promising practice to monitor and study
biodiversity change of natural ecosystems, providing large amounts of data with
minimal interference with the environment. Deep learning models are currently
used to automate classification of organisms into taxonomic units. However,
imprecision in these classifiers introduce a measurement noise that is
difficult to control and can significantly hinder the analysis and
interpretation of data. In our study, we show that this limitation can be
overcome by ensembles of Data-efficient image Transformers (DeiTs), which
significantly outperform the previous state of the art (SOTA). We validate our
results on a large number of ecological imaging datasets of diverse origin, and
organisms of study ranging from plankton to insects, birds, dog breeds, animals
in the wild, and corals. On all the data sets we test, we achieve a new SOTA,
with a reduction of the error with respect to the previous SOTA ranging from
18.48% to 87.50%, depending on the data set, and often achieving performances
very close to perfect classification. The main reason why ensembles of DeiTs
perform better is not due to the single-model performance of DeiTs, but rather
to the fact that predictions by independent models have a smaller overlap, and
this maximizes the profit gained by ensembling. This positions DeiT ensembles
as the best candidate for image classification in biodiversity monitoring.
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