Effective Pipeline For Compressing Deep Object Detectors

To alleviate the deployment of deep object detectors with large model capacity and complex computation, an effective model compression pipeline is designed in this paper. Firstly, attributed to the refined soft filter pruning, 3D filters of each convolution layer are regularized and then auto-pruned to achieve an overall more compact backbone. Afterwards, the branch layers of the object detector are simplified with the usage of simple residual blocks and fixed channel deletion. Experimental results reveal the superior effectiveness and generality of proposed pipeline for compressing detection models. Notably, on the generic detection dataset, proposed pipeline reduces more than 67% model size on RefineDet with negligible mAP loss. Moreover, the pipeline decreases more than 73% model size on the PyramidBox face detector with little loss of hard AP on WIDER FACE.