<p>This is a tricky question to answer but usually theoretical CNN is able to do. It is mainly dependent on the training data itself. In case of a child vs adult, you can gather a dataset that includes alot of variances in sizes and ages in order to make sure that you will have CNN model that able to find patterns and generalize at the end. At the end, the CNN will learn many other features that make the classification scale or size invariant (In dependent of Size) such as shapes,colors, clothes and face features ....etc. Such Intra-class classification problems, it is not easily tackled with traditional supervised learning and therefore some researchers are applying an approach called "<a href="https://arxiv.org/abs/1412.6622" rel="nofollow noreferrer">Deep Metric Learning</a>". </p>

<blockquote>
  <p>Metric learning is the task of learning a distance function over objects. A metric or distance function has to obey four axioms: non-negativity, identity of indiscernibles, symmetry and subadditivity (or the triangle inequality). In practice, metric learning algorithms ignore the condition of identity of indiscernibles and learn a pseudo-metric.<a href="https://en.wikipedia.org/wiki/Similarity_learning" rel="nofollow noreferrer">Wiki Definition</a></p>
</blockquote>

<p><a href="https://www.arxiv-vanity.com/papers/1708.01682/" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/KdIYX.png" alt="Example for Metric Learning"></a></p>
Can a CNN recognize the difference in size if the images are the same?
<p>Could a CNN tell the difference between different size range of the same organism? Like a puppy vs a adult or a child vs an adult? Or more like a large fly vs a small fly, where they look identical but one is just larger than the other?</p>