Comments (5)
The EMD Loss calculates the minimum loss between the two sets. The single loss between each element in the two sets is measured by SmoothL1 and softmax Cross Entropy.
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For each element in anchor set
, two boxes will be regressed, then you apply smoothl1 and cross entropy to it ....It's almost the same as traditional loss do.In this way , I can also regard traditional single loss as EMD Los between anchor set and pred_boxes set while num of pred_boxes is equal to that in anchor set , right?
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In details , given anchor set A
in shape of (1, 1, 4) (bs=1, num_anchor=1, 4=xywh),and pred boxes set B
in shape of (1, 2, 4), when we calc EMD loss for regression, we can obtaion res = smoothl1(A[0,0], B[0,0]) + smoothl1(A[0,0], B[0,1])
.
for single loss, pred boxes set B
will be in shape of (1, 1, 4), and result will be res = smoothl1(A[0,0], B[0,0])
.
sorry to disturb you, is there something wrong with my understanding?Thank you.
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The anchor is regressed by two parallel prediction head. The anchor A is predicted as P_head0 and P_head1.
The loss is between P_heads and ground truths.
The EMDLoss calculate the min ((L(p_head0, B0) + L(p_head1, B1)),(L(p_head0, B1) + L(p_head1, B0))). Ensure that the network can always be optimized in the optimal direction.
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thanks for your reply, I make it to understand now
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