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Hi ! Excellent work on making this implementation of Deformable Convolution.
When I used DCN to replace some of my backbone conv to train an network for object tracking problem, I found that simply replaced the conv to DCN didn't improve performance as expect and it decreased for about 6-8% on both precession and succese.
When I check the offset value during training, offset mean can reach about 100 or even larger.And the responds feature were much more ambiguous than normal convolution.
So is there any solutions to fix or contain the offsets during learning, so the receptive field can be limited to a fixed area?
The text was updated successfully, but these errors were encountered:
Hi ! Excellent work on making this implementation of Deformable Convolution.
When I used DCN to replace some of my backbone conv to train an network for object tracking problem, I found that simply replaced the conv to DCN didn't improve performance as expect and it decreased for about 6-8% on both precession and succese.
When I check the offset value during training, offset mean can reach about 100 or even larger.And the responds feature were much more ambiguous than normal convolution.
So is there any solutions to fix or contain the offsets during learning, so the receptive field can be limited to a fixed area?
The text was updated successfully, but these errors were encountered: