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Datasets/iNaturalist

iNaturalist

ImagesCustom (non-commercial)Introduced 2018-01-01

The iNaturalist 2017 dataset (iNat) contains 675,170 training and validation images from 5,089 natural fine-grained categories. Those categories belong to 13 super-categories including Plantae (Plant), Insecta (Insect), Aves (Bird), Mammalia (Mammal), and so on. The iNat dataset is highly imbalanced with dramatically different number of images per category. For example, the largest super-category “Plantae (Plant)” has 196,613 images from 2,101 categories; whereas the smallest super-category “Protozoa” only has 381 images from 4 categories.

Source: Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Image Source: https://github.com/visipedia/inat_comp/tree/master/2017

Benchmarks

Fine-Grained Image Classification/Top 1 AccuracyImage Classification/Top 1 AccuracyImage Classification/Top 5 AccuracyImage Classification/Top 3 ErrorImage Classification/OverallImage Retrieval/R@1Image Retrieval/R@16Image Retrieval/R@32Image Retrieval/R@5

Related Benchmarks

iNaturalist (227-way multi-shot)/Few-Shot Image Classification/AccuracyiNaturalist (227-way multi-shot)/Image Classification/AccuracyiNaturalist 2018/Few-Shot Image Classification/Top-1 AccuracyiNaturalist 2018/Generalized Few-Shot Classification/Top-1 AccuracyiNaturalist 2018/Generalized Few-Shot Learning/Top-1 AccuracyiNaturalist 2018/Image Classification/Number of paramsiNaturalist 2018/Image Classification/Top-1 AccuracyiNaturalist 2018/Long-tail Learning/Top-1 AccuracyiNaturalist 2018 - 1-shot/Few-Shot Image Classification/Top 1 AccuracyiNaturalist 2018 - 1-shot/Image Classification/Top 1 AccuracyiNaturalist 2018 - 10-shot/Few-Shot Image Classification/Top 1 AccuracyiNaturalist 2018 - 10-shot/Image Classification/Top 1 AccuracyiNaturalist 2018 - 5-shot/Few-Shot Image Classification/Top 1 AccuracyiNaturalist 2018 - 5-shot/Image Classification/Top 1 AccuracyiNaturalist 2019/Image Classification/Number of paramsiNaturalist 2019/Image Classification/Top-1 AccuracyiNaturalist 2019/Image Generation/FID

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Papers
603
Benchmarks
9

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Few-Shot Image ClassificationFine-Grained Image ClassificationImage ClassificationImage GenerationImage RetrievalLong-tail LearningTest Agnostic Long-Tailed Learning