The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
The iNaturalist Species Classification and Detection Dataset
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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“Biases are not limited to the geographical provenance of images: the type of sensors used (RGB vs. infrared or thermal), the species they depict, and the imbalance in the number of individuals observed per species55,132 must also be considered when training or using models to avoid potentially catastrophic drop-offs in accuracy”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [132]
In the citing paper
“For example, many open imagery repositories such as Artportalen.se, Naturgucker.de, and Waarneming.nl collect images from specific regions, and most contributions on iNaturalist132 (see Table 2) come from the Northern hemisphere.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [132]
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Flagged first, then by citations
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- zhang, 2017, The iMaterialist Challenge 2017 Dataset, Workshop at CVPR
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Self-citationSelf-Supervised Learning of Pretext-Invariant Representations
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“Classification 2,040 6,149 102 Oxford-IIIT Pets [23] 3,680 3,669 37 iNaturalist 2019 [34] 265,240 3,003 1,010 CIFAR-10 [16] 50,000 10,000 10 CIFAR-100 [16] 50,000 10,000 100”
Method
Metadata
- 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)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 8769-8778
- ISSN
- 1063-6919 (ISSN-L)
- 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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