<p>There is absolutely no general answer to this question, no prinicipal method to determine these hyperparameters is known. A conventional approach is to look for similar problems and deep learning architectures which have already been shown to work. Than a suitable architecture can be developed by experimentation. However conventional kernel size's are 3x3, 5x5 and 7x7.</p>

<p>Otherwise, there are paper about this <a href="https://arxiv.org/pdf/1606.02228v2.pdf" rel="nofollow noreferrer">1</a> and <a href="https://github.com/ducha-aiki/caffenet-benchmark" rel="nofollow noreferrer">2</a>, you may want to take look to see the art of choosing hyper parameters in CNN.</p>
How do we select kernels in CNN? (Convolutional Neural Networks)
<p>Before being fed to the neural network there are kernels applied to images for feature extraction.But, how do we understand that a particular kernel will help to extract the required feature for neural network.</p>