SSD Network Compression Fusing Weight and Filter Pruning

Abstract

Abstract: Object detection is an important research direction in the field of computer vision.In recent years,deep lear-ning has achieved great breakthroughs in object detection which is based on the video.Deep learning has powerful ability of feature learning and feature representation.The ability enables it to automatically learn,extract and utilize relevant features.However,complex network structure makes the deep learning model have a large scale of parameter.The deep neural network is both computationally intensive and memory intensive.Single Shot MultiBox Detector300 (SSD300),a single-shot detector,produces markedly superior detection accuracy and speed by using a single deep neural network.But it is difficult to deploy it on object detection systems with limited hardware resources.To address this limitation,the fusing method of weight pruning and filter pruning was proposed to reduce the storage requirement and inference time required by neural networks without affecting its accuracy.Firstly,in order to reduce the number of excessive weight parameters in the model of deep neural network,the weight pruning method is proposed.Network connections is pruned,in which weight is unimportant.Then,to reduce the large computation in convolution layer,the redundant filters are pruned according to the percentage of effective weights in each layer.Finally,the pruned neural network is trained to restore its detection accuracy.To verify the effectiveness of the method,the SSD300 was validated on caffe which is the convolutional neural network framework.After compression and acceleration,the storage of SSD300 neural network required is 12.5MB and the detection speed is 50FPS.The fusion of weight and filter pruning achieves the result by 2× speed-up,which reduces the storage required by SSD300 by 8.4×,as little increase of error as possible.The fusing method of weight and filter pruning makes it possible for SSD300 to be embedded in intelligent systems to detect and track objects.

Key words: Deep neural networks, Filter pruning, Network compression and acceleration, Single-shot multi-box detector (SSD), Weight pruning

CLC Number:

TP183

HAN Jia-lin, WANG Qi-qi, YANG Guo-wei, CHEN Jun, WANG Yi-zhong. SSD Network Compression Fusing Weight and Filter Pruning[J].Computer Science, 2019, 46(11): 272-276.

References

[1]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE,2014:580-587.
[2]GIRSHICK R.Fast R-CNN[C]∥Proceedings of the IEEE Conference on International Conference on Computer Vision.Boston:IEEE,2015:1440-1448.
[3]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[4]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2016:779-788.
[5]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotmultibox detector[C]∥Proceedings of European Conference on Computer Vision.Amsterdam:Springer International Publishing,2016:21-37.
[6]HAN S,POOL J,TRAN J,et al.Learning both weights andconnections for efficient neural networks[M]∥Neural Information Processing Systems.Morgan Kaufmann Publishers Inc,2015:1135-1143.
[7]HAN S,POOL J,DALLY W J,et al.Deep Compression:compressing deep neuralnetworks with pruning,trained quantization and huffman coding[C]∥Proceedings of Conference on Learning Representations.San Juan:IEEE,2016:233-242.
[8]MOHAMMAD R,VICENTE O,JOSEPH R,et al.XOR-Net:ImageNet Classification Using Binary Convolutional Neural Networks[C]∥Proceedings of European Conference on Computer Vision.Amsterdam:ECCV,2016:525-542.
[9]MATTHIEU C,ITAY H,DANIEL S,et al.Binarized NeuralNetworks:Training Neural Networks with Weights and Activations Constrained to +1 or －1[EB/OL].https://arxiv.org/abs/1704.04861.pdf.
[10]GEOFFREY H,ORIOL V,JEFF D,et al.Distilling the knowledge in a Neural Network[C]∥Proceedings of Conference on Advances in Neural Infermation Processing Systems.Montreal:IEEE,2014:2644-2652.
[11]BHARAT BHUSAN S,VINEETH N.B.Deep Model Compression:Distilling Knowledge from Noisy Teachers[EB/OL].https://arxiv.org/abs/1610.09650.pdf.
[12]MAX J,ANDREA V,ANDREW Z,et al.Speeding up Convolutional Neural Networks with Low Rank Expansions[J].Computer Science,2014,4(4):1-7.
[13]VIKAS S,TARA N S,SANJIV K,et al.Structured Transforms for Small-Footprint Deep Learning[EB/OL].https://arxiv.org/abs/1510.01722.pdf.
[14]WEN W,WU C,WANG Y,et al.Learning structured sparsity in deep neural networks[M]∥Advances in Neural Information Processing Systems.Berlin:Springer,2016:2074-2082.
[15]LIU Z,SHEN Z,HUANG G,et al.Learning efficient convolutional networks through network slimming[C]∥Proceedings of the IEEE International Conference on Computer Vision(ICCV).IEEE,2017:2755-2763.
[16]HE Y,ZHANG X,SUN J,et al.Channel pruning for accelerating very deep neural networks [EB/OL].https://arxiv.org/abs/1707.06168.pdf.
[17]IANDOLA F N,HAN S,MOSKEWICZ M W,et al.SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size[C]∥Proceedings of International Conference on Learning Representations.San Juan:ICLR,2016.
[18]HOWARD A G,ZHU M,CHEN B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications [EB/OL].https://arxiv.org/abs/1704.04861.pdf.
[19]ZHANG X,ZHOU X,LIN M,et al.ShuffleNet:an extremely efficient convolutional neural network for mobile devices [EB/OL].https:// arxiv.org/abs/1707.01083.pdf.
[20]EVERINGHAM M,VAN G L,WILLIAMS C K I,et al.Thepascal visual object classes (voc) challenge[J].International journal of computer vision,2010,88(2):303-338.
[21]HANSON S J,PRATT L Y.Comparing biases for minimal network construction with back-propagation[M]∥Neural Information Processing Systems.Morgan Kaufmann Publishers Inc,1989:177-185.
[22]CUN Y L,DENKER J S,SOLLA S A,et al.Optimal brain damage[C]∥Neural Information Processing Systems.Morgan Kaufmann Publishers Inc,1990:598-605.
[23]HASSIBI B,STORK D G.Second Order derivatives for network pruning:optimal brain surgeon[C]∥Neural Information Processing Systems.Morgan Kaufmann Publishers Inc,1992:164-171.
[24]HAN Y F,JIANG T H,MA Y P,et al.Compression of deep neural networks [J].Application Research of Computers,2018,35(10):2894-2897.(in Chinese)
韩云飞,蒋同海,马玉鹏,等.深度神经网络的压缩研究[J].计算机应用研究,2018,35(10):2894-2897.
[25]焦李成.深度学习、优化与识别[M].北京:清华大学出版社,2017:104.

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