Created by Hongyang Gao, Zhengyang Wang, and Shuiwang Ji at Texas A&M University.
ChannelNets are compact and efficent CNN via Channel-wise convolutions. It has been accepted in NIPS2018.
Detailed information about ChannelNets is provided in https://papers.nips.cc/paper/7766-channelnets-compact-and-efficient-convolutional-neural-networks-via-channel-wise-convolutions.pdf.
@inproceedings{gao2018channelnets,
title={ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions},
author={Gao, Hongyang and Wang, Zhengyang and Ji, Shuiwang},
booktitle={Advances in Neural Information Processing Systems},
pages={5203--5211},
year={2018}
}
| Models | Top-1 | Params | FLOPs |
|---|---|---|---|
| GoogleNet | 0.698 | 6.8m | 1550m |
| VGG16 | 0.715 | 128m | 15300m |
| AlexNet | 0.572 | 60m | 720m |
| SqueezeNet | 0.575 | 1.3m | 833m |
| 1.0 MobileNet | 0.706 | 4.2m | 569m |
| ShuffleNet 2x | 0.709 | 5.3m | 524m |
| ChannelNet-v1 | 0.705 | 3.7m | 407m |
All network hyperparameters are configured in main.py.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent