dasguptar / bcnn.pytorch Goto Github PK

I refered to your and Hao Mood's code and trained the BCNN model fine tuning all layers,
and the best test accuracy I can reach was ~73%/~61% with and without pretrained VGG16.
Is it easy to reach the accuracy of 84% you report?

I used almost the same hyperparameter setting, except the batch-size.
Due to memory constraint, I can only set batch-size as 12.
I doubt the small batch size would hurt the training but have no evidence.
Since the VGG16 I used didn't include BN layers, and people just said
small batch size can provide noise in training to prevent from poor generalization.

Because small batch size increase the variance of gradient,
so I also tried to tune the lr rate in order to adjust that, but still can't improve the result.

Could you give me some advice on how to reach the 84% accuracy?
or confirm that it is not possible to reach 84% accuracy when batch size is 12.

Is it correct to use torch.sqrt(outputs + 1e-5) since outputs may have negative values? Thanks!

https://github.com/dasguptar/bcnn.pytorch/blob/master/bcnn/model.py#L32

outputs = torch.sign(outputs) * torch.sqrt(outputs + 1e-5) # signed square root normalization

Thank you for providing the implementation of BCNN.
After reading your code, I'm cofused with the model setting that
it just train single VGG model and outer product the output and its transpose.
Shouldn't BCNN train two independent VGG models and outer product their outputs?
Am I misunderstanding something?