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Dear @kuangliu,
In some Cases (on the training process) the num_pos will be equal to 0 (in the ssd_loss.py script).
Then an error will be occurred. So to address the issue, I have added the below code:
...
ifnum_pos>0:
print('loc_loss: %.3f | cls_loss: %.3f'% (loc_loss.item()/num_pos, cls_loss.item()/num_pos), end=' | ')
loss= (loc_loss+cls_loss)/num_poselse:
print('Number of Positive Samples is 0.')
loss= (loc_loss+cls_loss) # (??? I don't know correct or not)returnloss
My question is that, in this situations the loss must be equal to zero (as the main paper of SSD mentioned) or a big number? Related Links for above issue:
1- #16
2- GluonCV-Advanced Notes about SSD Training
The text was updated successfully, but these errors were encountered:
OK I had the same problem, didn't think of setting it to zero, I had set N to 1, but that clearly seems the error. SSD paper has made it clear I think.
Dear @kuangliu,
In some Cases (on the training process) the
num_pos
will be equal to 0 (in thessd_loss.py
script).Then an error will be occurred. So to address the issue, I have added the below code:
My question is that, in this situations the loss must be equal to zero (as the main paper of SSD mentioned) or a big number?
Related Links for above issue:
1- #16
2- GluonCV-Advanced Notes about SSD Training
The text was updated successfully, but these errors were encountered: