PyTorch Implementation of GhostNet

Reproduction of GhostNet architecture as described in GhostNet: More Features from Cheap Operations by Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu on ILSVRC2012 benchmark with PyTorch framework.

Pretrained Models

Architecture	# Parameters	MFLOPs	Top-1 / Top-5 Accuracy (%)
GhostNet 1.0x	5.181M	140.77	73.636 / 91.228

from ghostnet import ghostnet

net = ghostnet()
net.load_state_dict(torch.load('pretrained/ghostnet_1x-9c40f966.pth'))

Training Strategy

• batch size 1024 on 8 GPUs
• Initial learning rate 0.4
• weight decay 0.00004
• dropout rate 0.2
• no weight decay on BN

We keep the above settings as the same and conduct experiments with different training techniques below for ablation and reproduction. During the warmup phase, learning rate linearly ramps up from 0.1 to 0.4.

epoch	LR annealing	warmup	label smooth	Top-1 / Top-5 Accuracy (%)
240	linear	×	×	72.318 / 90.670
360	linear	×	×	72.458 / 90.780
240	cosine	√	×	72.772 / 90.902
240	cosine	√	√	73.636 / 91.228

Citation

@inproceedings{Han_2020_CVPR,
  title={GhostNet: More Features from Cheap Operations},
  author={Han, Kai and Wang, Yunhe and Tian, Qi and Guo, Jianyuan and Xu, Chunjing and Xu, Chang},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year={2020}
}