Open prakashjayy opened 6 years ago
does test loss also very less? Focal loss can be very less by compressing a easy classes but it doesn't mean that not working. I am also trying to reproduce with VOC dataset.
Q1. => I think you should count the # of instances including background class to normalize.
of course long time ago, I tried. I remember that performance was poor on VOC dataset.
I can’t remember exact numbers... hm, it was around 40mAP or 60mAP. haha I know the gab between two are large lol. It’s almost 1 years ago. plz understand.
@EvanAlbee
x = x[:, 1:]
doesn't mean that exclude all negative matches.
Negative can be defined "predicted as positives but true labels are background cases" in here.
Well, we still consider x[:, 0]
as negative samples but may useless to reduce false positives.
Hi,
I have implemented your code and it worked properly but have the following concerns
My sudo code works like this cls_targets = [batch_size, anchor_boxes, classes] # classes is 21 (voc_labels+background) [16, 67995, 21] cls_preds = [batch_size, anchor_boxes] # anchor_boxes number ranges from -1 to 20 [67995, 21]
Now I remove all the anchor boxes with -1 (ignore_boxes) cls_targets = [batch_size valid_anchor_boxes, classes] # [54933, 21] cls_preds = [batch_size valid_anchor_boxes, classes] # [54933, 21] This is one hot encoding vector
Now, I followed your code and implemented focal loss as it is but My loss values are coming very less. Like random values is giving a score of 0.12 and quickly the loss is going 0.0012 and small
is der I am missing something:
Question1: I am still not getting quite write, if I should use 0 as my background class and how normalization is done while focal loss is applied.