I managed to finetune a ResNet-50 via dede for a two-class dataset, but I can't quite deploy it correctly — it's always predicting one class, even on the images from the training set. While training, I'm getting ~0.75 precision and recall, so a massive bias is impossible (my dataset is balanced), therefore I'm suspecting an error in deployment. I'm using dede's .prototxt for both train (https://github.com/beniz/deepdetect/blob/master/templates/caffe/resnet_50/resnet_50.prototxt
, I added lr_mult: 0 to all layers except for the last FC) and deploy (https://github.com/beniz/deepdetect/blob/master/templates/caffe/resnet_50/deploy.prototxt)
. Also, this is the script I'm using for setting up the prediction: https://gist.github.com/alkamid/c56e590292a634fd4b89d3a0aada7ea6
. I'm thinking now, doesn't the deploy file need to know about mean.binaryproto? If I'm training on mean-subtracted images, surely the prediction should also subtract the mean image before passing an image through the net?