Training and validating on images with different resolution in Keras
You need to make sure that your network input is of shape (None,None,3), which means your network accepts an input color image of arbitrary size.
I'm using Keras to build a convolutional neural net to perform regression from microscopic images to 2D label data (for counting). I'm looking into training the network on smaller patches of the microscopic data (where the patches are the size of the receptive field). The problem is, the fit() method requires validation data to be of the same size as the input. Instead, I'm hoping to be able to validate on entire images (not patches) so that I can validate on my entire validation set and compare the results to other methods I've used so far.
One solution I found was to alternate between fit() and evaluate() each epoch. However, I was hoping to be able to observe these results using Tensorboard. Since evaluate() doesn't take in callbacks, this solution isn't ideal. Does anybody have a good way validating on full-resolution images while training on patches?