  We present a simple and effective learning technique that significantly
improves mAP of YOLO object detectors without compromising their speed. During
network training, we carefully feed in localization information. We excite
certain activations in order to help the network learn to better localize. In
the later stages of training, we gradually reduce our assisted excitation to
zero. We reached a new state-of-the-art in the speed-accuracy trade-off. Our
technique improves the mAP of YOLOv2 by 3.8% and mAP of YOLOv3 by 2.2% on
MSCOCO dataset.This technique is inspired from curriculum learning. It is
simple and effective and it is applicable to most single-stage object
detectors.
