Fashion-MNIST
Fashion-MNIST is a dataset designed as a more challenging replacement for the original MNIST dataset. It consists of 70,000 grayscale images of 10 different fashion items such as T-shirts, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots, each sized at 28×28 pixels. Like MNIST, it is divided into a training set of 60,000 images and a test set of 10,000 images. Created by Zalando Research, Fashion-MNIST serves the same purpose as the traditional MNIST—facilitating benchmarking and experimentation in machine learning and computer vision—but with a focus on fashion products. This dataset is commonly used in academic and research settings to develop, train, and test advanced image classification algorithms.
Description:
- Content: Includes 70,000 grayscale images of fashion items.
- Resolution: Each image is 28×28 pixels.
- Classes: 10 different categories, including T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, and Ankle boot.
- Split: Divided into 60,000 images for training and 10,000 for testing.
- Purpose: Designed as a more challenging replacement for the traditional MNIST dataset, used for benchmarking machine learning models in computer vision.
Dataset for Image Classification
The field of computer vision has witnessed remarkable progress in recent years, largely driven by the availability of large-scale datasets for image classification tasks. These datasets play a pivotal role in training and evaluating machine learning models, enabling them to recognize and categorize visual content with increasing accuracy.
In this article, we will discuss some of the famous datasets used for image classification.