  In this paper, we propose an efficient and fast object detector which can
process hundreds of frames per second. To achieve this goal we investigate
three main aspects of the object detection framework: network architecture,
loss function and training data (labeled and unlabeled). In order to obtain
compact network architecture, we introduce various improvements, based on
recent work, to develop an architecture which is computationally light-weight
and achieves a reasonable performance. To further improve the performance,
while keeping the complexity same, we utilize distillation loss function. Using
distillation loss we transfer the knowledge of a more accurate teacher network
to proposed light-weight student network. We propose various innovations to
make distillation efficient for the proposed one stage detector pipeline:
objectness scaled distillation loss, feature map non-maximal suppression and a
single unified distillation loss function for detection. Finally, building upon
the distillation loss, we explore how much can we push the performance by
utilizing the unlabeled data. We train our model with unlabeled data using the
soft labels of the teacher network. Our final network consists of 10x fewer
parameters than the VGG based object detection network and it achieves a speed
of more than 200 FPS and proposed changes improve the detection accuracy by 14
mAP over the baseline on Pascal dataset.
