Fashion MNIST explained
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. 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%.
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.
See also
External links
Notes and References
- Xiao. Han. Rasul. Kashif. Vollgraf. Roland. 2017-09-15. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. cs.LG . 1708.07747.
- Web site: Shenwai. Tanushree. 2021-09-07. A New Google AI Research Study Discovers Anomalous Data Using Self Supervised Learning. 2021-10-07. MarkTechPost. en-US.
- News: Fashion-MNIST: Year In Review · Han Xiao Tech Blog - Neural Search & AI Engineering. 2022-01-30. hanxiao.io. en.
- Web site: Basic classification: Classify images of clothing TensorFlow Core. 2021-10-07. TensorFlow. en.
- Web site: Build software better, together. 2022-01-30. GitHub. en.
- Web site: Papers using Fashion-MNIST (till 09.18). 2022-01-30. Google Docs. en-US.
- Meshkini. Khatereh. Platos. Jan. Ghassemain. Hassan. 2020. Kovalev. Sergey. Tarassov. Valery. Snasel. Vaclav. Sukhanov. Andrey. 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. en. Cham. Springer International Publishing. 85–95. 10.1007/978-3-030-50097-9_10. 978-3-030-50097-9. 226778948.
- Book: Kayed. Mohammed. Anter. Ahmed. Mohamed. Hadeer. 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE) . Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture . February 2020. https://ieeexplore.ieee.org/document/9047776. 238–243. 10.1109/ITCE48509.2020.9047776. 978-1-7281-4801-4. 214691687.
- Book: Bhatnagar. Shobhit. Ghosal. Deepanway. Kolekar. Maheshkumar H.. 2017 Fourth International Conference on Image Information Processing (ICIIP) . Classification of fashion article images using convolutional neural networks . December 2017. https://ieeexplore.ieee.org/document/8313740. 1–6. 10.1109/ICIIP.2017.8313740. 978-1-5090-6733-6. 3888338.
- Kadam. Shivam S.. Adamuthe. Amol C.. Patil. Ashwini B.. 2020. CNN Model for Image Classification on MNIST and Fashion-MNIST Dataset. Journal of Scientific Research. 64. 2. 374–384. 10.37398/JSR.2020.640251. 226435631.
- Tanveer. Muhammad Suhaib. Khan. Muhammad Umar Karim. Kyung. Chong-Min. 2020-06-16. Fine-Tuning DARTS for Image Classification. cs.CV . 2006.09042.
- Web site: Papers with Code - Fashion-MNIST Benchmark (Image Classification). 2022-01-30. paperswithcode.com. en.
- 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. en. 361. 6406. 1004–1008. 10.1126/science.aat8084. 30049787. 1804.08711. 2018Sci...361.1004L. 13753997. 0036-8075.