The iNaturalist Species Classification and Detection Dataset
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.
Originalsprog | Engelsk |
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Tidsskrift | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Sider (fra-til) | 8769-8778 |
Antal sider | 10 |
ISSN | 1063-6919 |
DOI | |
Status | Udgivet - 14 dec. 2018 |
Eksternt udgivet | Ja |
Begivenhed | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, USA Varighed: 18 jun. 2018 → 22 jun. 2018 |
Konference
Konference | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
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Land | USA |
By | Salt Lake City |
Periode | 18/06/2018 → 22/06/2018 |
Bibliografisk note
Publisher Copyright:
© 2018 IEEE.
ID: 301825294