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]
The dataset contains 70,000 28x28 grayscale images of fashion products from 10 categories from a dataset of Zalando article images, with 7,000 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 edit
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 edit
References edit
- ^ a b c 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].
- ^ Shenwai, Tanushree (2021-09-07). "A New Google AI Research Study Discovers Anomalous Data Using Self Supervised Learning". MarkTechPost. Retrieved 2021-10-07.
- ^ "Fashion-MNIST: Year In Review · Han Xiao Tech Blog - Neural Search & AI Engineering". hanxiao.io. Retrieved 2022-01-30.
- ^ "Basic classification: Classify images of clothing | TensorFlow Core". TensorFlow. Retrieved 2021-10-07.
- ^ "Build software better, together". GitHub. Retrieved 2022-01-30.
- ^ "Papers using Fashion-MNIST (till 09.18)". Google Docs. Retrieved 2022-01-30.
- ^ Meshkini, Khatereh; Platos, Jan; Ghassemain, Hassan (2020). Kovalev, Sergey; Tarassov, Valery; Snasel, Vaclav; Sukhanov, Andrey (eds.). "An Analysis of Convolutional Neural Network for Fashion Images Classification (Fashion-MNIST)". Proceedings of the Fourth International Scientific Conference "Intelligent Information Technologies for Industry" (IITI'19). Advances in Intelligent Systems and Computing. 1156. Cham: Springer International Publishing: 85–95. doi:10.1007/978-3-030-50097-9_10. ISBN 978-3-030-50097-9. S2CID 226778948.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ Tanveer, Muhammad Suhaib; Khan, Muhammad Umar Karim; Kyung, Chong-Min (2020-06-16). "Fine-Tuning DARTS for Image Classification". arXiv:2006.09042 [cs.CV].
- ^ "Papers with Code - Fashion-MNIST Benchmark (Image Classification)". paperswithcode.com. Retrieved 2022-01-30.
- ^ 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.