Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. However, in real world applications, models often encounter out-of-distribution data. The VisDA21 competition invites methods that can adapt to novel test distributions and handle distributional shifts. Our task is object classification, but we measure accuracy on novel domains, rather than the traditional in-domain benchmarking. Teams will be given labeled source data and unlabeled target data from a different distribution (such as novel viewpoints, backgrounds, image quality). In addition, the target data may have missing and/or novel classes. Successful approaches will improve classification accuracy of known categories on target-domain data while learning to deal with missing and/or unknown categories.