arXiv (Cornell University)· 2017· Preprint
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- 6,097citations
- 2017year
Short summary
Fashion-MNIST, a dataset of 70,000 28x28 grayscale images of 10 fashion product categories, is introduced as a drop-in replacement for the original MNIST dataset to benchmark machine learning algorithms.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Fashion-MNIST, a dataset with 70,000 images of 10 fashion categories.
- Images are 28x28 grayscale, with 7,000 per category.
- Designed as a drop-in replacement for the MNIST dataset.
- Maintains identical image size, data format, and train/test split structure as MNIST.
AI-generated from the title and abstract; the full text is not read.
Abstract
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. 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. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist
The authors' abstract, as published at the source. arXiv (Cornell University), 2017 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science