<p>Because square images are pleasing to the eye. But there are applications on non-square images when domain requires it. For instance SVHN original dataset is an image of several digits, and hence rectangular images are used as input to convnet, as <a href="http://arxiv.org/abs/1312.6082" rel="nofollow">here</a></p>
Why are inputs for convolutional neural networks always squared images?
<p>I have been doing deep learning with CNN for a while and I realize that the inputs for a model are always squared images. </p>

<p>I see that neither convolution operation or neural network architecture itself require such property. </p>

<p>So, what is the reason for that?</p>