Many machine learning algorithms assume that the training data and the test data follow the same distribution. However, such assumptions are often violated in real-world machine learning problems. In this paper, we propose SHIFT15M, a dataset that can be used to properly evaluate models in situations where the distribution of data changes between training and testing. The SHIFT15M dataset has several good properties: (i) Multiobjective. Each instance in the dataset has several numerical values that can be used as target variables. (ii) Large-scale. The SHIFT15M dataset consists of 15million fashion images. (iii) Coverage of types of dataset shifts. SHIFT15M contains multiple dataset shift problem settings (e.g., covariate shift or target shift). SHIFT15M also enables the performance evaluation of the model under various magnitudes of dataset shifts by switching the magnitude. In addition, we provide software to handle SHIFT15M in a very simple way: https://github.com/st-tech/zozo-shift15m.
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