  In image classification tasks, the evaluation of models' robustness to
increased dataset shifts with a probabilistic framework is very well studied.
However, object detection (OD) tasks pose other challenges for uncertainty
estimation and evaluation. For example, one needs to evaluate both the quality
of the label uncertainty (i.e., what?) and spatial uncertainty (i.e., where?)
for a given bounding box, but that evaluation cannot be performed with more
traditional average precision metrics (e.g., mAP). In this paper, we adapt the
well-established YOLOv3 architecture to generate uncertainty estimations by
introducing stochasticity in the form of Monte Carlo Dropout (MC-Drop), and
evaluate it across different levels of dataset shift. We call this novel
architecture Stochastic-YOLO, and provide an efficient implementation to
effectively reduce the burden of the MC-Drop sampling mechanism at inference
time. Finally, we provide some sensitivity analyses, while arguing that
Stochastic-YOLO is a sound approach that improves different components of
uncertainty estimations, in particular spatial uncertainties.
