Hi All,
I am running into issues when trying to run batch normalization. Specifically, I train a network to differentiate between two different pixel labels. The results converge and approach about 90% accuracy. When I test images using this network, the results are pretty good (using models_iter_20000.caffemodel). However, when I try to run normalization, the results get much much worse. Does anyone have a reason why this might happen? Or am I maybe messing something up during the normalization step?
Thanks!
Hi All, I am running into issues when trying to run batch normalization. Specifically, I train a network to differentiate between two different pixel labels. The results converge and approach about 90% accuracy. When I test images using this network, the results are pretty good (using models_iter_20000.caffemodel). However, when I try to run normalization, the results get much much worse. Does anyone have a reason why this might happen? Or am I maybe messing something up during the normalization step? Thanks!