fashion-mnist

Digits/MNIST is a very, very bad dataset for CV demos because it's too easy and like done here, you can do a logistic regression on raw pixel values and get sufficiently good results. That's also the reason why Fashion MNIST was created, to give ML demos some difficulty: https://github.com/zalandoresearch/fashion-mnist For more typical image classification problems, you can get >90% of the way for image classification on any arbitrary image dataset and with much less code by using CLIPVision image embeddings as a model input to your classification algorithm of choice.

Or Fashion-MNIST https://github.com/zalandoresearch/fashion-mnist :)

To test a machine learning algorithm, one can use "Fashion-MNIST": https://github.com/zalandoresearch/fashion-mnist "Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits."