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L1 loss or SmoothL1Loss?

See original GitHub issue

Hi, I’ve been reading through the code and I found that L1 loss is used instead of Smooth L1 loss for localization loss. This is quite different from the paper’s procedure, where as far as I know SSD uses Smooth L1 loss.

https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/model.py#L549

self.smooth_l1 = nn.L1Loss()

https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/model.py#L612

loc_loss = self.smooth_l1(predicted_locs[positive_priors], true_locs[positive_priors]) # (), scalar

My questions are:

Has anyone tried changing the loss function to SmoothL1Loss as implemented in PyTorch as of right now?
If it has been tried, is the result similar to what SSD achieves?

Thank you in advance.

Issue Analytics

State:
Created 3 years ago
Comments:7

Top GitHub Comments

1reaction

jonathan016commented, May 23, 2021

With the experiment results provided by @adityag6994, I believe my questions have been answered. Closing this issue for now. Thanks a lot @adityag6994!

1reaction

adityag6994commented, May 23, 2021

That makes sense now. Thank you @jonathan016