CIFAR-10

CIFAR-10

CIFAR-10

Image dataset

The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research.[1][2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.[3] The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.[4]

Computer algorithms for recognizing objects in photos often learn by example. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works.

CIFAR-10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009. When the dataset was created, students were paid to label all of the images.[5]

Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10.

Research papers claiming state-of-the-art results on CIFAR-10

This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR-10 dataset. Not all papers are standardized on the same pre-processing techniques, like image flipping or image shifting. For that reason, it is possible that one paper's claim of state-of-the-art could have a higher error rate than an older state-of-the-art claim but still be valid.

More information Paper title, Error rate (%) ...

Benchmarks

CIFAR-10 is also used as a performance benchmark for teams competing to run neural networks faster and cheaper. DAWNBench has benchmark data on their website.

See also

References

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    "AI Progress Measurement". Electronic Frontier Foundation. 2017-06-12. Retrieved 2017-12-11.
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    "Popular Datasets Over Time | Kaggle". www.kaggle.com. Retrieved 2017-12-11.
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    Hope, Tom; Resheff, Yehezkel S.; Lieder, Itay (2017-08-09). Learning TensorFlow: A Guide to Building Deep Learning Systems. O'Reilly Media, Inc. pp. 64–. ISBN 9781491978481. Retrieved 22 January 2018.
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    Angelov, Plamen; Gegov, Alexander; Jayne, Chrisina; Shen, Qiang (2016-09-06). Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Springer International Publishing. pp. 441–. ISBN 9783319465623. Retrieved 22 January 2018.
  5. [5]
    Krizhevsky, Alex (2009). "Learning Multiple Layers of Features from Tiny Images" (PDF).
  6. [6]
    "Convolutional Deep Belief Networks on CIFAR-10" (PDF).
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    Goodfellow, Ian J.; Warde-Farley, David; Mirza, Mehdi; Courville, Aaron; Bengio, Yoshua (2013-02-13). "Maxout Networks". arXiv:1302.4389 [stat.ML].
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    Zagoruyko, Sergey; Komodakis, Nikos (2016-05-23). "Wide Residual Networks". arXiv:1605.07146 [cs.CV].
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    Zoph, Barret; Le, Quoc V. (2016-11-04). "Neural Architecture Search with Reinforcement Learning". arXiv:1611.01578 [cs.LG].
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    Graham, Benjamin (2014-12-18). "Fractional Max-Pooling". arXiv:1412.6071 [cs.CV].
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    Huang, Gao; Liu, Zhuang; Weinberger, Kilian Q.; van der Maaten, Laurens (2016-08-24). "Densely Connected Convolutional Networks". arXiv:1608.06993 [cs.CV].
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    Gastaldi, Xavier (2017-05-21). "Shake-Shake regularization". arXiv:1705.07485 [cs.LG].
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    Dutt, Anuvabh (2017-09-18). "Coupled Ensembles of Neural Networks". arXiv:1709.06053 [cs.CV].
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    Yamada, Yoshihiro; Iwamura, Masakazu; Kise, Koichi (2018-02-07). "Shakedrop Regularization for Deep Residual Learning". IEEE Access. 7: 186126–186136. arXiv:1802.02375. doi:10.1109/ACCESS.2019.2960566. S2CID 54445621.
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    Terrance, DeVries; W., Taylor, Graham (2017-08-15). "Improved Regularization of Convolutional Neural Networks with Cutout". arXiv:1708.04552 [cs.CV].{{cite arXiv}}: CS1 maint: multiple names: authors list (link)
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    Real, Esteban; Aggarwal, Alok; Huang, Yanping; Le, Quoc V. (2018-02-05). "Regularized Evolution for Image Classifier Architecture Search with Cutout". arXiv:1802.01548 [cs.NE].
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    Nguyen, Huu P.; Ribeiro, Bernardete (2020-07-31). "Rethinking Recurrent Neural Networks and other Improvements for Image Classification". arXiv:2007.15161 [cs.CV].
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    Cubuk, Ekin D.; Zoph, Barret; Mane, Dandelion; Vasudevan, Vijay; Le, Quoc V. (2018-05-24). "AutoAugment: Learning Augmentation Policies from Data". arXiv:1805.09501 [cs.CV].
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    Wistuba, Martin; Rawat, Ambrish; Pedapati, Tejaswini (2019-05-04). "A Survey on Neural Architecture Search". arXiv:1905.01392 [cs.LG].
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    Huang, Yanping; Cheng, Yonglong; Chen, Dehao; Lee, HyoukJoong; Ngiam, Jiquan; Le, Quoc V.; Zhifeng, Zhifeng (2018-11-16). "GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism". arXiv:1811.06965 [cs.CV].
  21. [21]
    Kabir, Hussain (2023-05-05). "Reduction of Class Activation Uncertainty with Background Information". arXiv:2305.03238 [cs.CV].
  22. [22]
    Dosovitskiy, Alexey; Beyer, Lucas; Kolesnikov, Alexander; Weissenborn, Dirk; Zhai, Xiaohua; Unterthiner, Thomas; Dehghani, Mostafa; Minderer, Matthias; Heigold, Georg; Gelly, Sylvain; Uszkoreit, Jakob; Houlsby, Neil (2021). "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale". International Conference on Learning Representations. arXiv:2010.11929.

Similar datasets

  • CIFAR-100: Similar to CIFAR-10 but with 100 classes and 600 images each.
  • ImageNet (ILSVRC): 1 million color images of 1000 classes. Imagenet images are higher resolution, averaging 469x387 resolution.
  • Street View House Numbers (SVHN): Approximately 600,000 images of 10 classes (digits 0–9). Also 32x32 color images.
  • 80 million tiny images dataset: CIFAR-10 is a labeled subset of this dataset.
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