Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge


This dataset contains a total of 5,089 categories, across 579,184 training images and 95,986 validation images. For the training set, the distribution of images per category follows the observation frequency of that category by the iNaturalist community.

Although the original dataset contains some images with bounding boxes, currently, only image-level annotations are provided (single label/image). In addition, the organizers have not published the test labels, so we only provide the test images (label = -1).

Split Examples
'test' 182,707
'train' 579,184
'validation' 95,986
  • Features:
    'id': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=5089),
    'supercategory': ClassLabel(shape=(), dtype=tf.int64, num_classes=13),


  • Citation:
author = {
Van Horn, Grant and Mac Aodha, Oisin and Song, Yang and Cui, Yin and Sun, Chen
and Shepard, Alex and Adam, Hartwig and Perona, Pietro and Belongie, Serge},
title = {The INaturalist Species Classification and Detection Dataset},
booktitle = {
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}