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pith:2017:MZV6RJ7NMLEVM2AZBLY3EU6KUX
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Han Xiao, Kashif Rasul, Roland Vollgraf

Fashion-MNIST supplies a drop-in replacement for MNIST using 28x28 fashion images.

arxiv:1708.07747 v2 · 2017-08-25 · cs.LG · cs.CV · stat.ML

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Claims

C1strongest claim

Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits.

C2weakest assumption

That the fashion images provide a meaningfully harder yet still accessible benchmark that will be widely adopted by the community in place of MNIST.

C3one line summary

Fashion-MNIST is a new benchmark dataset of 70,000 fashion product images that serves as a direct drop-in replacement for the original MNIST dataset while being more challenging.

References

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[1] D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3642--3649. IEEE 2012
[2] EMNIST: an extension of MNIST to handwritten letters 2017 · arXiv:1702.05373
[3] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, page 2009
[4] A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009 2009
[5] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 0 (11): 0 2278--2324, 1998 1998

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First computed 2026-07-04T22:12:34.838563Z
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Canonical hash

666be8a7ed62c95668190af1b253caa5fd2704f34dafc49e9e199971793876ea

Aliases

arxiv: 1708.07747 · arxiv_version: 1708.07747v2 · doi: 10.48550/arxiv.1708.07747 · pith_short_12: MZV6RJ7NMLEV · pith_short_16: MZV6RJ7NMLEVM2AZ · pith_short_8: MZV6RJ7N
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MZV6RJ7NMLEVM2AZBLY3EU6KUX \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 666be8a7ed62c95668190af1b253caa5fd2704f34dafc49e9e199971793876ea
Canonical record JSON 
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