Cited by 1,451Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningSmart Agriculture and AI

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The iNaturalist Species Classification and Detection Dataset

Van Horn, Grant, Mac Aodha, Oisin, Song, Yang, Cui, Yin, Sun, Chen, Shepard, Alex, Adam, Hartwig, Perona, Pietro, Belongie, Serge·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

DOI 10.1109/cvpr.2018.00914

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Abstract

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.

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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Self-citation
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
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Research works printed but not linked: 5

uedai, 2017, Naturalist Research-grade Observations iNaturalist org Occurrence Dataset
zhang, 2017, The iMaterialist Challenge 2017 Dataset, Workshop at CVPR
khosla, 2011, Novel dataset for fine-grained image categorization, Workshop at CVPR
krasin, 2016, Openimages: A public dataset for large-scale multi-label and multiclass image classification, Dataset
welinder, 2010, Caltech-UCSD Birds 200

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Authors: Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S.
Affiliations: Google (United States) (US)
Cornell Tech (US)
California Institute of Technology (US)
ORCID: Song, Yang
Cui, Yin
Sun, Chen
Adam, Hartwig
Perona, Pietro
Belongie, Serge
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher: IEEE
Published: 2018-06
Pages: 8769-8778
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr.2018.00914
OpenAlex: W2797977484
Semantic Scholar: 05eb6eb4ea7d2b332295dfa5aeb64d5f47c1e628
Cited by: 1,451
References: 49
Citations by year: 2026: 150 · 2025: 261 · 2024: 261 · 2023: 264 · 2022: 164 · 2021: 190 · 2020: 100 · 2019: 55 · 2018: 6
FWCI: 26.45
Type: conference paper
Language: en
Open access: closed
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords: species classification, species detection, iNaturalist, class imbalance, citizen science, plant images, animal imagery, few-shot learning, image classification, object detection

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The iNaturalist Species Classification and Detection Dataset

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