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# Dataset Card for Fashion-MNIST
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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
<!-- Provide the basic links for the dataset. -->
- **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
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**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},
}
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