Is there a way of pre-determining whether a CNN model will perform better than another? I developed a CNN for image analysis. I've around 100K labeled images.  I'm getting a accuracy around 85% and a validation accuracy around 82%, so it looks like the model generalize better than fitting. So, I'm playing with different hyper-parameters: number of filters, number of layers, number of neurons in the dense layers, etc. 

For every test, I'm using all the training data, and it is very slow and time consuming.  

Is there a way to have an early idea about if a model will perform better than another?
I would like you to try with following changes in the model. 


Introduce batch normalization layer in the model. 
Try with the batch size of 32 - 64
Use different architecture, Like VGG, Resnet, etc..


There is no full proof answer to this question but you can get best by trying some know strategies.