imagenet_lt

Warning: Manual download required. See instructions below.

• 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.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/zhmiao/OpenLongTailRecognition-OLTR

• Source code: tfds.datasets.imagenet_lt.Builder

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 5.21 MiB

• Dataset size: 20.92 GiB

• Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
  manual_dir should contain two files: ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar. You need to register on http://www.image-net.org/download-images in order to get the link to download the dataset.

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	50,000
'train'	115,846
'validation'	20,000

• Feature structure:

FeaturesDict({
    'file_name': Text(shape=(), dtype=string),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=1000),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
file_name	Text		string
image	Image	(None, None, 3)	uint8
label	ClassLabel		int64

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

\
@inproceedings{openlongtailrecognition,
  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)},
  year={2019},
  url={https://github.com/zhmiao/OpenLongTailRecognition-OLTR}
}