Fashion MNIST is a dataset of 60,000 28x28 grayscale images of 10 fashion categories, with 6,000 images per class. It was created as a more challenging alternative to the traditional MNIST dataset, which contains images of handwritten digits.
The 10 fashion categories in the dataset are:
T-shirt/top Trouser Pullover Dress Coat Sandal Shirt Sneaker Bag Ankle boot
The images in the dataset are 28x28 grayscale, with pixel values ranging from 0 to 255. The labels are integers from 0 to 9, representing the respective fashion categories.
Fashion MNIST can be used for various machine learning tasks, such as image classification, object detection, and segmentation. It is a popular dataset for testing machine learning algorithms and is often used as a benchmark for performance.
Fashion MNIST is available in popular machine learning libraries such as TensorFlow and Keras, and can be easily loaded into your project. If you prefer to work with raw data, the dataset can also be downloaded from here.
Fashion MNIST is licensed under the MIT license.
Fashion MNIST is a valuable resource for machine learning practitioners and researchers. It provides a challenging yet accessible dataset for testing and evaluating algorithms, and can be used for a wide range of tasks.