What is Fashion MNIST?
Fashion-MNIST is a dataset developed by Zalando Research as a modern alternative to the original MNIST dataset. It comprises 70,000 grayscale images categorized into 10 fashion-related items. Each image is 28×28 pixels, providing a uniform format for machine learning model input. The dataset is divided into a training set of 60,000 images and a test set of 10,000 images.
The ten categories in Fashion MNIST are:
- T-shirt/top
- Trouser
- Pullover
- Dress
- Coat
- Sandal
- Shirt
- Sneaker
- Bag
- Ankle boot
Characteristics of Fashion MNIST Dataset
Here are the key characteristics of the Fashion-MNIST dataset in bullet points:
- Images are preprocessed and normalized, with pixel values ranging from 0 to 255.
- Fashion-MNIST introduces real-world complexity with variations in lighting, pose, and background clutter.
- The dataset exhibits class imbalance, with some categories having more images than others.
How to load Fashion MNIST dataset using PyTorch?
In machine learning, datasets are essential because they serve as benchmarks for comparing and assessing the performance of different algorithms. Fashion MNIST is one such dataset that replaces the standard MNIST dataset of handwritten digits with a more difficult format. The article explores the Fashion MNIST dataset, including its characteristics, uses, and how can we load it using PyTorch.