Fashion MNIST

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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. [1] [2] 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. [3]

Contents

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. [1] 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. [4]

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%. [1]

The GitHub repository has collected over 4000 stars and is referred to more than 400 repositories, 1000 commits and 7000 code snippets. [5]

Numerous machine learning algorithms [6] have used the dataset as a benchmark, [7] [8] [9] [10] with the top algorithm [11] achieving 96.91% accuracy in 2020 according to the benchmark rankings website. [12] 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. [13] Google, University of Cambridge, IBM Research, Université de Montréal, and Peking University are the repositories most published institutions as of 2021.[ citation needed ]

See also

References

  1. 1 2 3 Xiao, Han; Rasul, Kashif; Vollgraf, Roland (2017-09-15). "Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms". arXiv: 1708.07747 [cs.LG].
  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 [cs.CV].
  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.
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