evgenykashin / srmnet Goto Github PK

PyTorch implementation of "SRM : A Style-based Recalibration Module for Convolutional Neural Networks"

License: MIT License

Python 100.00%

computer-vision deep-learning pytorch srm

srmnet's Issues

Do not apply weight decay on SRM parameters

From authors: "Since SRM module learns to how to utilize mean and std statistics, it is better not to impose weight decay regularization." About SRM parameters only.

ImageNet training

Add an ImageNet version of ResNets. Train them to get benchmarks results.

Train ImageNet models

Add comparison of models and fill results table

I want to use the Class SRMLAyer with channel=128 but it gives this error:
RuntimeError: Given groups=128, weight of size [128, 1, 2], expected input[1, 64, 2] to have 128 channels, but got 64 channels instead

How can I fix it?

could you share srm_resnet50.pth,thanks

The performance of SRM-Net on the cifar10.

Hi, thanks for your work. When I performed an experiment on the cifar10 using srmnet-32, I only got 91.2 accuracy, which is far away from the result you post (92.9). Could you obtain this accuracy stably during your experiments?

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