  The Open Images Dataset contains approximately 9 million images and is a
widely accepted dataset for computer vision research. As is common practice for
large datasets, the annotations are not exhaustive, with bounding boxes and
attribute labels for only a subset of the classes in each image. In this paper,
we present a new set of annotations on a subset of the Open Images dataset
called the MIAP (More Inclusive Annotations for People) subset, containing
bounding boxes and attributes for all of the people visible in those images.
The attributes and labeling methodology for the MIAP subset were designed to
enable research into model fairness. In addition, we analyze the original
annotation methodology for the person class and its subclasses, discussing the
resulting patterns in order to inform future annotation efforts. By considering
both the original and exhaustive annotation sets, researchers can also now
study how systematic patterns in training annotations affect modeling.
