Fashion-MNIST is a popular dataset used for training machine learning models, particularly in the field of computer vision. It consists of 70,000 grayscale images of clothing items, such as shirts, shoes, and bags, categorized into 10 different classes. Each image is 28x28 pixels in size, making it a simplified version of the original MNIST dataset, which features handwritten digits. The dataset is divided into 60,000 training images and 10,000 test images, allowing researchers to evaluate their models effectively. Fashion-MNIST serves as a benchmark for various algorithms, helping to advance the development of image recognition technologies in the fashion industry and beyond.