MNIST is a widely used dataset of handwritten digits that contains 60,000 handwritten digits for training a machine learning model and 10,000 handwritten digits for testing the model. It was introduced in 1998 and has become a standard benchmark for classification tasks. It is also called the “Hello, World” dataset as it’s very easy to use. MNIST was derived from an even larger dataset, the NIST Special Database 19 which not only contains digits but also uppercase and lowercase handwritten letters.
In the MNIST dataset each digit is stored in a grayscale image with a size of 28x28 pixels. In the following you can see the first 10 digits from the training set:
Today, the dataset is considered as too simple (e.g. see tweet of Ian Goodfellow) for testing the modern, very complex deep learning models with up to billions of parameters. However, the dataset is still useful. For instance it is useful for quickly testing new implementation of algorithms. If the tested model achieves a high accuracy on MNIST chances are that the implementation is correct. If the algorithm doesn’t work on MNIST, it won’t work at all. Second, MNIST is well suited for machine learning beginners who, for instance…
