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Dataset Card for Fashion-MNIST
Dataset Details
Dataset Description
Fashion-MNIST is a dataset of 70,000 grayscale images, each 28×28 pixels, representing 10 different classes of clothing and accessories. It serves as a drop-in replacement for the original MNIST dataset but provides a more challenging benchmark for machine learning models. The dataset was introduced by Zalando Research to address the limitations of MNIST, which primarily contains handwritten digits.
Dataset Sources
- Homepage: https://github.com/zalandoresearch/fashion-mnist?tab=readme-ov-file#license
- Paper: Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.
Dataset Structure
Total images: 70,000
Classes: 10 (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)
Splits:
Train: 60,000 images
Test: 10,000 images
Image specs: PNG format, 28×28 pixels, Grayscale
Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/fashion-mnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/fashion-mnist", split="test", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]
image.show() # Display the image
print(f"Label: {label}")
Citation
BibTeX:
@online{xiao2017/online, author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, date = {2017-08-28}, year = {2017}, eprintclass = {cs.LG}, eprinttype = {arXiv}, eprint = {cs.LG/1708.07747}, }