  Deep learning approaches have achieved unprecedented performance in visual
recognition tasks such as object detection and pose estimation. However,
state-of-the-art models have millions of parameters represented as floats which
make them computationally expensive and constrain their deployment on hardware
such as mobile phones and IoT nodes. Most commonly, activations of deep neural
networks tend to be sparse thus proving that models are over parametrized with
redundant neurons. Model compression techniques, such as pruning and
quantization, have recently shown promising results by improving model
complexity with little loss in performance. In this work, we extended pruning,
a compression technique that discards unnecessary model connections, and weight
sharing techniques for the task of object detection. With our approach, we are
able to compress a state-of-the-art object detection model by 30.0% without a
loss in performance. We also show that our compressed model can be easily
initialized with existing pre-trained weights, and thus is able to fully
utilize published state-of-the-art model zoos.
