[GitHub] sbodenstein commented on issue #8582: Yolo2 operator

2017-11-10 Thread GitBox
sbodenstein commented on issue #8582: Yolo2 operator
URL: https://github.com/apache/incubator-mxnet/pull/8582#issuecomment-343462534
 
 
   One more comment on this PR: is there anyway of verifying that the gradient 
is correctly calculated?


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[GitHub] sbodenstein commented on issue #8582: Yolo2 operator

2017-11-09 Thread GitBox
sbodenstein commented on issue #8582: Yolo2 operator
URL: https://github.com/apache/incubator-mxnet/pull/8582#issuecomment-343229939
 
 
   > I am working on object detection operators decomposed and implemented 
using gluon blocks/nnvm operators that is highly modular and customizable.
   
   @zhreshold: OK great! That will be very nice for MXNet to have. Do you have 
a rough timeframe for those?


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[GitHub] sbodenstein commented on issue #8582: Yolo2 operator

2017-11-08 Thread GitBox
sbodenstein commented on issue #8582: Yolo2 operator
URL: https://github.com/apache/incubator-mxnet/pull/8582#issuecomment-342901844
 
 
   One issue for us: this layer is an 'output layer' and hence doesn't support 
out grads into the layer. This is an unfortunate restriction, as it rules out 
things like example weighting. In addition, it does not return the actual 
per-example loss, which is non-trivial to compute. I think (but stand to be 
corrected, @piiswrong) that MXNet is moving away from these `output layers`. 
   
   One option: split this into a `yolo_region_layer` (based on DarkNet 
https://github.com/pjreddie/darknet/blob/master/src/region_layer.c), and then a 
separate loss layer that returns the loss.
   
   What are others thoughts on this? (could also have this 'output layer' and 
then also the separated region and loss layers, but this seems like a lot of 
code duplication).


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