Computer vision is applied in an ever expanding range of applications, many of which require custom training data to perform well. We present a novel interface for rapid collection and labeling of training images to improve computer vision- based object detectors. LabelAR leverages the spatial tracking capabilities of an AR-enabled camera, allowing users to place persistent bounding volumes that stay centered on real-world objects. The interface then guides the user to move the cam- era to cover a wide variety of viewpoints. We eliminate the need for post-hoc manual labeling of images by automatically projecting 2D bounding boxes around objects in the images as they are captured from AR-marked viewpoints. In a user study with 12 participants, LabelAR significantly outperforms existing approaches in terms of the trade-off between model performance and collection time.