  We study the problem of object detection from a novel perspective in which
annotation budget constraints are taken into consideration, appropriately
coined Budget Aware Object Detection (BAOD). When provided with a fixed budget,
we propose a strategy for building a diverse and informative dataset that can
be used to optimally train a robust detector. We investigate both optimization
and learning-based methods to sample which images to annotate and what type of
annotation (strongly or weakly supervised) to annotate them with. We adopt a
hybrid supervised learning framework to train the object detector from both
these types of annotation. We conduct a comprehensive empirical study showing
that a handcrafted optimization method outperforms other selection techniques
including random sampling, uncertainty sampling and active learning. By
combining an optimal image/annotation selection scheme with hybrid supervised
learning to solve the BAOD problem, we show that one can achieve the
performance of a strongly supervised detector on PASCAL-VOC 2007 while saving
12.8% of its original annotation budget. Furthermore, when $100\%$ of the
budget is used, it surpasses this performance by 2.0 mAP percentage points.
