This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/
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the training graph and "inference1" layer weight #75
I want to check whether my training goes well, so could you show your training graph?
especially, my initial loss value is about 3000~4000 but in some iteration the loss is 200000 and like that.
and
I checked inference1 layer filter's weight,
when I see as imagesc in matlab, your model shows diagonal
but my initialized model shows diagonal but moved one pixel vertically for all the diagonal pixels.
is there anything I have to consider to train?
Hello. I'm training the model with fcn8s model (https://github.com/shelhamer/fcn.berkeleyvision.org/tree/master/voc-fcn8s).
I want to check whether my training goes well, so could you show your training graph? especially, my initial loss value is about 3000~4000 but in some iteration the loss is 200000 and like that. and I checked inference1 layer filter's weight, when I see as imagesc in matlab, your model shows diagonal but my initialized model shows diagonal but moved one pixel vertically for all the diagonal pixels. is there anything I have to consider to train?