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  • Description:

ImageNet-LT is a subset of original ImageNet ILSVRC 2012 dataset. The training set is subsampled such that the number of images per class follows a long-tailed distribution. The class with the maximum number of images contains 1,280 examples, whereas the class with the minumum number of images contains only 5 examples. The dataset also has a balanced validation set, which is also a subset of the ImageNet ILSVRC 2012 training set and contains 20 images per class. The test set of this dataset is the same as the validation set of the original ImageNet ILSVRC 2012 dataset.

The original ImageNet ILSVRC 2012 dataset must be downloaded manually, and its path should be set with --manual_dir in order to generate this dataset.

Split Examples
'test' 50,000
'train' 115,846
'validation' 20,000
  • Feature structure:
    'file_name': Text(shape=(), dtype=object),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=1000),
  • Feature documentation:
Feature Class Shape Dtype Description
file_name Text object
image Image (None, None, 3) uint8
label ClassLabel int64


  • Citation:
  title={Large-Scale Long-Tailed Recognition in an Open World},
  author={Liu, Ziwei and Miao, Zhongqi and Zhan, Xiaohang and Wang, Jiayun and Gong, Boqing and Yu, Stella X.},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},