  Object detection aims at high speed and accuracy simultaneously. However,
fast models are usually less accurate, while accurate models cannot satisfy our
need for speed. A fast model can be 10 times faster but 50\% less accurate than
an accurate model. In this paper, we propose Adaptive Feeding (AF) to combine a
fast (but less accurate) detector and an accurate (but slow) detector, by
adaptively determining whether an image is easy or hard and choosing an
appropriate detector for it. In practice, we build a cascade of detectors,
including the AF classifier which make the easy vs. hard decision and the two
detectors. The AF classifier can be tuned to obtain different tradeoff between
speed and accuracy, which has negligible training time and requires no
additional training data. Experimental results on the PASCAL VOC, MS COCO and
Caltech Pedestrian datasets confirm that AF has the ability to achieve
comparable speed as the fast detector and comparable accuracy as the accurate
one at the same time. As an example, by combining the fast SSD300 with the
accurate SSD500 detector, AF leads to 50\% speedup over SSD500 with the same
precision on the VOC2007 test set.
