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 nonensemble
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.
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 nonensemble
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
