d-li14 / ghostnet.pytorch

73.6% GhostNet 1.0x pre-trained model on ImageNet

ghostnet.pytorch's Issues

What's the specific parameters of learning rate annealing?

Hello, I set the learning rate annealing parameter like this: for every 5 epochs passed, the learning rate was multiplied by 0.5, from 0.4 to 0. I'm currently training up to 40 epochs, top-1 accuracy is 67.010%, top-5 accuracy is 86.918%, and now the increase is very small, do I need to continue training? Could you tell me your specific parameters of linear LR annealing, which's accuracy is 72.318%/90.670%?

Where is training code?

Hello, thank you for you work! I could not find your training code to reproduce your results. Where can I find this?

Training Code for ImageNet

Hi @d-li14

Could you also share the training code for ghostNet?
I would like to train the model from scratch with some minor improvements of my own for my research.
Thanks in advance!

A question about size of model(ghostnet_1x-f97d70db.pth) and parameters.

Sorry to bother you again, I'm confused about the size of model. The file 'ghostnet_1x-f97d70db.pth' is about 20M, but the parameters is only about 5M as you mentioned. What's the difference between them and how to measure the size of parameters? What's more, how to compute the FLOPs in the model?

请问怎么训练自己的数据呢

Custom Weight Initialization Effect

I noticed you use code for custom weight initialization:

ghostnet.pytorch/ghostnet.py

Lines 162 to 169 in 83274b7

	def _initialize_weights(self):
	for m in self.modules():
	if isinstance(m, nn.Conv2d):
	nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
	elif isinstance(m, nn.BatchNorm2d):
	m.weight.data.fill_(1)
	m.bias.data.zero_()

I've not seen this before anywhere. Is there a reason behind this specific strategy? Do you know the effect this has on the training, and have you compared this with the pytorch default weight initialization? Thank you!

Thank for your job!