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The Fashion MNIST dataset is a large freely available database of fashion images that is commonly used for training and testing various machine learning systems. Fashion-MNIST was intended to serve as a replacement for the original MNIST database for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits.

The dataset contains 60,000 28x28 grayscale images of fashion products from 10 categories from a dataset of Zalando article images, with 6,000 images per category. The training set consists of 60,000 images and the test set consists of 10,000 images. The dataset is commonly included in standard machine learning libraries.

History

The set of images in the Fashion MNIST database was created in 2017 to pose a more challenging classification task than the simple MNIST digits data, which saw performance reaching upwards of 99.7%.

The GitHub repository has collected over 4000 stars and is referred to more than 400 repositories, 1000 commits and 7000 code snippets.

Numerous machine learning algorithms have used the dataset as a benchmark, with the top algorithm achieving 96.91% accuracy in 2020 according to the benchmark rankings website. The dataset was also used as a benchmark in the 2018 Science paper using all optical hardware to classify images at the speed of light. Google, University of Cambridge, IBM Research, Université de Montréal, and Peking University are the repositories most published institutions as of 2021.

See also

• List of datasets for machine learning research
• MNIST database

References

1. ^ Xiao, Han; Rasul, Kashif; Vollgraf, Roland (2017-09-15). "Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms". arXiv:1708.07747 .
2. Shenwai, Tanushree (2021-09-07). "A New Google AI Research Study Discovers Anomalous Data Using Self Supervised Learning". MarkTechPost. Retrieved 2021-10-07.
3. "Fashion-MNIST: Year In Review · Han Xiao Tech Blog - Neural Search & AI Engineering". hanxiao.io. Retrieved 2022-01-30.
4. "Basic classification: Classify images of clothing | TensorFlow Core". TensorFlow. Retrieved 2021-10-07.
5. "Build software better, together". GitHub. Retrieved 2022-01-30.
6. "Papers using Fashion-MNIST (till 09.18)". Google Docs. Retrieved 2022-01-30.
7. Meshkini, Khatereh; Platos, Jan; Ghassemain, Hassan (2020). "An Analysis of Convolutional Neural Network for Fashion Images Classification (Fashion-MNIST)". In Kovalev, Sergey; Tarassov, Valery; Snasel, Vaclav; Sukhanov, Andrey (eds.). Proceedings of the Fourth International Scientific Conference "Intelligent Information Technologies for Industry" (IITI'19). Advances in Intelligent Systems and Computing. Vol. 1156. Cham: Springer International Publishing. pp. 85–95. doi:10.1007/978-3-030-50097-9_10. ISBN 978-3-030-50097-9. S2CID 226778948.
8. Kayed, Mohammed; Anter, Ahmed; Mohamed, Hadeer (February 2020). "Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture". 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). pp. 238–243. doi:10.1109/ITCE48509.2020.9047776. ISBN 978-1-7281-4801-4. S2CID 214691687.
9. Bhatnagar, Shobhit; Ghosal, Deepanway; Kolekar, Maheshkumar H. (December 2017). "Classification of fashion article images using convolutional neural networks". 2017 Fourth International Conference on Image Information Processing (ICIIP). pp. 1–6. doi:10.1109/ICIIP.2017.8313740. ISBN 978-1-5090-6733-6. S2CID 3888338.
10. Kadam, Shivam S.; Adamuthe, Amol C.; Patil, Ashwini B. (2020). "CNN Model for Image Classification on MNIST and Fashion-MNIST Dataset" (PDF). Journal of Scientific Research. 64 (2): 374–384. doi:10.37398/JSR.2020.640251. S2CID 226435631.
11. Tanveer, Muhammad Suhaib; Khan, Muhammad Umar Karim; Kyung, Chong-Min (2020-06-16). "Fine-Tuning DARTS for Image Classification". arXiv:2006.09042 .
12. "Papers with Code - Fashion-MNIST Benchmark (Image Classification)". paperswithcode.com. Retrieved 2022-01-30.
13. Lin, Xing; Rivenson, Yair; Yardimci, Nezih T.; Veli, Muhammed; Luo, Yi; Jarrahi, Mona; Ozcan, Aydogan (2018-09-07). "All-optical machine learning using diffractive deep neural networks". Science. 361 (6406): 1004–1008. arXiv:1804.08711. Bibcode:2018Sci...361.1004L. doi:10.1126/science.aat8084. ISSN 0036-8075. PMID 30049787. S2CID 13753997.

External links

• Official Github Repository
• Tutorial to classify Fashion MNIST in Tensorflow

• Datasets in computer vision