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PeleeNet: An efficient DenseNet architecture for mobile devices
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README.md

PeleeNet

PeleeNet: An efficient DenseNet architecture for mobile devices

An implementation of PeleeNet in PyTorch. PeleeNet is an efficient Convolutional Neural Network (CNN) architecture built with conventional convolution. Compared to other efficient architectures,PeleeNet has a great speed advantage and esay to be applied to the computer vision tasks other than image classification.

For more information, check the paper: Pelee: A Real-Time Object Detection System on Mobile Devices (NeurIPS 2018)

Citation

If you find this work useful in your research, please consider citing:


@incollection{NIPS2018_7466,
title = {Pelee: A Real-Time Object Detection System on Mobile Devices},
author = {Wang, Robert J. and Li, Xiang and Ling, Charles X.},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {1963--1972},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices.pdf}
}


Results on ImageNet ILSVRC 2012

The table below shows the results on the ImageNet ILSVRC 2012 validation set, with single-crop testing.

Model FLOPs # parameters Top-1 Acc FPS (NVIDIA TX2)
MobileNet 569 M 4.2 M 70.0 136
ShuffleNet 2x 524 M 5.2 M 73.7 110
Condensenet (C=G=8) 274M 4.0M 71 40
MobileNet v2 300 M 3.5 M 72.0 123
ShuffleNet v2 1.5x 300 M 5.2 M 72.6 164
PeleeNet (our) 508 M 2.8 M 72.6 240
PeleeNet v2 (our) 621 M 4.4 M 73.9 245
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