  This paper presents a detection-aware pre-training (DAP) approach, which
leverages only weakly-labeled classification-style datasets (e.g., ImageNet)
for pre-training, but is specifically tailored to benefit object detection
tasks. In contrast to the widely used image classification-based pre-training
(e.g., on ImageNet), which does not include any location-related training
tasks, we transform a classification dataset into a detection dataset through a
weakly supervised object localization method based on Class Activation Maps to
directly pre-train a detector, making the pre-trained model location-aware and
capable of predicting bounding boxes. We show that DAP can outperform the
traditional classification pre-training in terms of both sample efficiency and
convergence speed in downstream detection tasks including VOC and COCO. In
particular, DAP boosts the detection accuracy by a large margin when the number
of examples in the downstream task is small.
