Fashion-MNIST Dataset

Zalando's Fashion-MNIST clothing classification dataset (Xiao et al., 2017).

Overview

  • Embedded: 100 samples (10 per class)
  • Full (hf-hub): 70,000 samples
  • Features: 784 pixels (28x28 grayscale)
  • Classes: 10 clothing categories
  • Task: Multi-class classification

Loading

use alimentar::datasets::{fashion_mnist, CanonicalDataset};

let dataset = fashion_mnist()?;
assert_eq!(dataset.len(), 100);
assert_eq!(dataset.num_features(), 784);
assert_eq!(dataset.num_classes(), 10);

Class Names

use alimentar::datasets::{FashionMnistDataset, FASHION_MNIST_CLASSES};

println!("{:?}", FASHION_MNIST_CLASSES);
// ["t-shirt/top", "trouser", "pullover", "dress", "coat",
//  "sandal", "shirt", "sneaker", "bag", "ankle boot"]

let name = FashionMnistDataset::class_name(0); // Some("t-shirt/top")
let name = FashionMnistDataset::class_name(9); // Some("ankle boot")

Full Dataset

[dependencies]
alimentar = { version = "0.1", features = ["hf-hub"] }

let full = FashionMnistDataset::load_full()?;

Train/Test Split

let dataset = fashion_mnist()?;
let split = dataset.split()?;
assert_eq!(split.train.len(), 80);
assert_eq!(split.test.len(), 20);

Reference

Xiao, H., Rasul, K., & Vollgraf, R. (2017). "Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms." arXiv:1708.07747.