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Understanding if classifiers generalize to out-of-sample datasets is acentral problem in machine learning. Microscopy images provide a standardizedway to measure the generalization capacity of image classifiers, as we canimage the same classes of objects under increasingly divergent, but controlledfactors of variation. We created a public dataset of 132,209 images of mousecells, COOS-7 (Cells Out Of Sample 7-Class). COOS-7 provides a classificationsetting where four test datasets have increasing degrees of covariate shift:some images are random subsets of the training data, while others are fromexperiments reproduced months later and imaged by different instruments. Webenchmarked a range of classification models using different representations,including transferred neural network features, end-to-end classification with asupervised deep CNN, and features from a self-supervised CNN. While mostclassifiers perform well on test datasets similar to the training dataset, allclassifiers failed to generalize their performance to datasets with greatercovariate shifts. These baselines highlight the challenges of covariate shiftsin image data, and establish metrics for improving the generalization capacityof image classifiers.

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers