  An increasing need of running Convolutional Neural Network (CNN) models on
mobile devices with limited computing power and memory resource encourages
studies on efficient model design. A number of efficient architectures have
been proposed in recent years, for example, MobileNet, ShuffleNet, and
MobileNetV2. However, all these models are heavily dependent on depthwise
separable convolution which lacks efficient implementation in most deep
learning frameworks. In this study, we propose an efficient architecture named
PeleeNet, which is built with conventional convolution instead. On ImageNet
ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy and over
1.8 times faster speed than MobileNet and MobileNetV2 on NVIDIA TX2. Meanwhile,
PeleeNet is only 66% of the model size of MobileNet. We then propose a
real-time object detection system by combining PeleeNet with Single Shot
MultiBox Detector (SSD) method and optimizing the architecture for fast speed.
Our proposed detection system2, named Pelee, achieves 76.4% mAP (mean average
precision) on PASCAL VOC2007 and 22.4 mAP on MS COCO dataset at the speed of
23.6 FPS on iPhone 8 and 125 FPS on NVIDIA TX2. The result on COCO outperforms
YOLOv2 in consideration of a higher precision, 13.6 times lower computational
cost and 11.3 times smaller model size.

Computer Vision and Pattern Recognition