Authors:
Lining Hu
and
Yongfu Li
Affiliation:
Department of Micro-Nano Electronics, MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, China
Keyword(s):
Object Detection, YOLO, MobileNets, Depthwise Separable Convolution, Model Compression, Prune.
Abstract:
Deep learning models have made significant breakthroughs in the performance of object detection. However, in the traditional models, such as Faster R-CNN and YOLO, the size of these networks make it too
difficult to be deployed on embedded mobile devices due to limited computation resources and tight power
budgets. Hence, we propose a new light-weight CNN based object detection model, Micro-YOLO based on
YOLOv3-Tiny, which achieves a signification reduction in the number of parameters and computation cost
while maintaining the detection performance. We propose to replace convolutional layers in the YOLOv3-tiny
network with the Depth-wise Separable convolution (DSConv) and the mobile inverted bottleneck convolution
with squeeze and excitation block (MBConv), and design a progressive channel-level pruning algorithm to
minimize the number of parameters and maximize the detection performance. Hence, the proposed MicroYOLO network reduces the number of parameters by 3.46× and multi
ply-accumulate operation (MAC) by
2.55× while slightly decreases the mAP evaluated on the COCO dataset by 0.7%, compared to the original
YOLOv3-tiny network.
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