Seeds Image – Introduction and Baseline Experiments with the New Labeled Benchmark for Machine Learning Tasks

The aim of this paper is to propose a new image data set for assessing the quality of solutions to machine learning tasks, in particular, deep neural networks. The data set is derived from X-ray images of wheat grains, in which three species, Kama, Rosa, and Canadian, are distinguished. In this paper, the structure of the data is presented in detail and ten pretrained deep neural networks are applied to identify individual wheat species. The Seeds Image Data Set, due to its compact nature, can compete with well-known and quite frequently used object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet, etc.). The compactness of the data set is based on a relatively small number of data instances, which shortens the rather time-consuming computing process. The proposed data set will be made available in a public repository, and the results presented will provide a starting point for other competing solutions for exploratory data analysis in the broad sense.