  Semi-supervised learning (SSL) has a potential to improve the predictive
performance of machine learning models using unlabeled data. Although there has
been remarkable recent progress, the scope of demonstration in SSL has mainly
been on image classification tasks. In this paper, we propose STAC, a simple
yet effective SSL framework for visual object detection along with a data
augmentation strategy. STAC deploys highly confident pseudo labels of localized
objects from an unlabeled image and updates the model by enforcing consistency
via strong augmentations. We propose experimental protocols to evaluate the
performance of semi-supervised object detection using MS-COCO and show the
efficacy of STAC on both MS-COCO and VOC07. On VOC07, STAC improves the
AP$^{0.5}$ from $76.30$ to $79.08$; on MS-COCO, STAC demonstrates $2{\times}$
higher data efficiency by achieving 24.38 mAP using only 5\% labeled data than
supervised baseline that marks 23.86\% using 10\% labeled data. The code is
available at https://github.com/google-research/ssl_detection/.
