  We introduce a dataset of annotations of temporal repetitions in videos. The
dataset, OVR (pronounced as over), contains annotations for over 72K videos,
with each annotation specifying the number of repetitions, the start and end
time of the repetitions, and also a free-form description of what is repeating.
The annotations are provided for videos sourced from Kinetics and Ego4D, and
consequently cover both Exo and Ego viewing conditions, with a huge variety of
actions and activities. Moreover, OVR is almost an order of magnitude larger
than previous datasets for video repetition. We also propose a baseline
transformer-based counting model, OVRCounter, that can localise and count
repetitions in videos that are up to 320 frames long. The model is trained and
evaluated on the OVR dataset, and its performance assessed with and without
using text to specify the target class to count. The performance is also
compared to a prior repetition counting model. The dataset is available for
download at: https://sites.google.com/view/openvocabreps/
