<p>There is a relationship between network size and input resolution, though it is a bit indirect. Increases in network size (i.e number of channels and the depth of the network) and input resolution are both computationally taxing. A recent paper proposes <a href="https://arxiv.org/abs/1905.11946" rel="nofollow noreferrer">EfficientNet</a> which parameterizes channel number, depth, and input resolution by a single parameter.</p>

<p>In essence, if you only have so much compute power to spend, you must choose how to distribute it over kernel size, depth, and resolution. Required compute power is linearly proportional to depth, each of the input side lengths, and the square of channel number. </p>
what is the relationship between spatial resolution of the image and size of the CNN architecture?
<p>I am working on different spatial resolutions of the image and thought to implement CNN architecture for each spatial resolution as resizing images affect the object details. Is there any particular relationship that can quantitatively explained between size of the network and spatial resolution of the image?</p>