How does image resolution affect result and accuracy in Keras?
1- Of course it will affect the training speed as the spatial dimensions is one of the most important key of the model speed performance.
2- We can say sure it'll affect the accuracy, but how much exactly that depends on many of other aspects like what objects are you classifying and what dataset are you working with.
I'm using Keras (with Tensorflow backend) for an image classification project. I have a total of almost 40 000 hi-resolution (1920x1080) images that I use as training input data. Training takes about 45 minutes and this is becoming a problem so I was thinking that I might be able to speed things up by lowering the resolution of the image files. Looking at the code (I didn't write it myself) it seems all images are re-sized to 30x30 pixels anyway before processing
I have two general questions about this.

Is it reasonable to expect this to improve the training speed?
Would resizing the input image files affect the accuracy of the image classification?