/ghostnet.pytorch

73.6% GhostNet 1.0x pre-trained model on ImageNet

Primary LanguagePythonMIT LicenseMIT

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}
}