Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts

Understand

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.