The MNIST dataset is the most commonly used dataset for testing the algorithms. But there are other datasets that are used to test image classification algorithms.
The Canadian Institute for Advanced Research (CIFAR)-10 dataset has 60,000 images with 50,000 images for training and 10,000 images for testing. The number of classes is 10. The image dimension is 32 pixels by 32 pixels. The following are randomly selected images from each of the class:
The images are tiny and just contain one object. The CIFAR-100 dataset contains the same number of images but with 100 classes. Hence, there are only 600 images per class. Each image comes with a super label and a fine label. This dataset is available at tf.keras.datasets if you wish to experiment.
