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The iNaturalist Species Classification and Detection Dataset

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Grant Van Horn
  • Oisin Mac Aodha
  • Yang Song
  • Yin Cui
  • Chen Sun
  • Alex Shepard
  • Hartwig Adam
  • Pietro Perona
  • Serge Belongie

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Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-5386-6420-9
ISBN (Print)978-1-5386-6421-6
Publication statusPublished - 17 Dec 2018
Event2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Publication series

PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075


Conference2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2018
CountryUnited States
CitySalt Lake City
Internet address


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.

    Research areas

  • crowdsourcing, embedded systems, image processing, learning (artificial intelligence), low dimensional embeddings, crowd workers, interpretable embeddings, model worker biases, noisy crowd, visual context, context embedding networks, CENs, data collections, crowd similarity, multi-dimensional concept, crowd embedding learning, image set, Visualization, Context modeling, Feature extraction, Training, Noise measurement, Data models, Crowdsourcing


2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition


Salt Lake City, United States

Event: Conference

ID: 122608162