Reproduction of PP-LCNet architecture as described in PP-LCNet: A Lightweight CPU Convolutional Neural Network by C. Cui. T. Gao, S. Wei et al (2021) with the PyTorch framework.
The official design is implemented with Paddle framework, the detail here
| Architecture | #Parameters | FLOPs | Top-1 Acc. (%) |
|---|---|---|---|
| PPLCNet_x0_25 | 1,522,960 | 18M | |
| PPLCNet_x0_35 | 1,646,888 | 29M | |
| PPLCNet_x0_5 | 1,881,864 | 47M | |
| PPLCNet_x0_75 | 2,359,792 | 99M | |
| PPLCNet_x1_0 | 2,955,816 | 161M | |
| PPLCNet_x1_5 | 4,504,136 | 342M | |
| PPLCNet_x2_0 | 6,526,824 | 590M | |
| PPLCNet_x2_5 | 9,023,880 | 906M |
Stay tuned for ImageNet pre-trained weights.
The implementation is heavily borrowed from HBONet or MobileNetV2, please kindly consider citing the following
@InProceedings{Li_2019_ICCV,
author = {Li, Duo and Zhou, Aojun and Yao, Anbang},
title = {HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}
@InProceedings{Sandler_2018_CVPR,
author = {Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
title = {MobileNetV2: Inverted Residuals and Linear Bottlenecks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}