2018

CINIC-10 is not ImageNet or CIFAR-10

Darlow, Luke N., Crowley, Elliot J., Antoniou, Antreas et al.

Understand

In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10.

  • It was compiled by combining CIFAR-10 with images selected and downsampled from the ImageNet database.
  • We present the approach to compiling the dataset, illustrate the example images for different classes, give pixel distributions for each part of the repository, and give some standard benchmarks for well known models.
  • Details for download, usage, and compilation can be found in the associated github repository.

Built on

  • Learning multiple layers of features from tiny images

    Alex Krizhevsky · 2009

    Earlier work this paper cites.

  • ImageNet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012

    Earlier work this paper cites.

  • Deep learning

    Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015

    Earlier work this paper cites.

  • ImageNet large scale visual recognition challenge

    Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015

    Earlier work this paper cites.

Similar

  • Very deep convolutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman · 2015

    Cited alongside, same era.

  • Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016

    Cited alongside, same era.

  • A downsampled variant of ImageNet as an alternative to the CIFAR datasets

    Original

    Patryk Chrabaszcz, Ilya Loshchilov, and Hutter Frank · 2017

    Cited alongside, same era.

  • CINIC-10 Github repository

    Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, and Amos J. Storkey

    Cited in the paper.

Then

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