Fashion MNIST is a popular dataset used in the field of deep learning for image classification tasks. It consists of 70,000 grayscale images of clothing items from ten different classes, such as shirts, dresses, and sneakers. The dataset's small image size (28x28 pixels) and simplicity make it an ideal starting point for beginners to explore and learn about deep learning techniques for computer vision. Researchers often use Fashion MNIST to benchmark and compare the performance of various deep learning models and algorithms. Deep learning using Fashion MNIST serves as a practical introduction to computer vision tasks and provides a foundation for tackling more complex image-related challenges. As researchers and practitioners gain experience, they can apply similar techniques to real-world problems, such as object recognition, medical imaging, and autonomous vehicles.
Dataset: Obtained from Kaggle. https://www.kaggle.com/datasets/zalando-research/fashionmnist