Class-balanced-loss-pytorch
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vandit15
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
Hi @vandit15, Thanks for sharing your code. In line 73, `weights = weights / np.sum(weights) * no_of_classes`, why is no_of_classes included here to normalise the weights? Any help would be...
typo
https://github.com/vandit15/Class-balanced-loss-pytorch/blob/921ccb8725b1eb0903b2c22a1a752a594fcae138/class_balanced_loss.py#L28 should be `alpha: A float tensor of size [num_classes]`
does someone has implement focal_loss with softmax correctly？could you recommend its link for me？thank you very much
Thanks for sharing your code. I have 4D tensor.. I just understand your code. Thanks!
why add this sum make all class weight the same 
Hi, thanks for your code sharing! I am now trying to understand how do you implement and I have something want to discuss. In your main function you have provide...
the keyword had a typo: 'weight' instead of 'weights'. in case weights requires grads, it needs to be detached first as BCEwithlogits is not implemented for grad tracked tensor
@vandit15 thanks for open-sourcing the code , is it possible to use Cb loss in the object detection or segmentation architecture ?? did you experiment it with any std architecture...
Instead calculating a weight for each batch, applying to class using pos_weight argument in `torch.nn.BCELoss(pos_weights=weights)` Simply, https://github.com/vandit15/Class-balanced-loss-pytorch/blob/master/class_balanced_loss.py#L71-L82 Are those line of codes same with ``` effective_num = 1.0 - np.power(beta,...
