title: Machine learning and images of different size
body: <p>Lets say I have a machine learning system that has learned to identify whether or not an image contains an object (for example a car). </p>
  
  <p>My question is the following, what to do when the system receives images of different sizes? the training set for example could be composed of images of 1000x700 px, but the real world is not so uniform, and one can expect images for 700x1000, 700x400 ... </p>
  
  <p>The first impulse would be to resize the image to have the same size as the training set, but that feels dirty and not really useful. </p>
  
  <p>Am I missing something obvious here? Also, even for the training set, I assume that it does not really  make sense to assume that every image would be of a fixed size (again, lets assume 1000x700 px). </p>
  
  <p>Also, if one uses the sliding window technique, the same problem might arise, as it is not easy to determine what is the correct window size. </p>
  
  <p>I imagine that this must be a relatively common problem, so there must be a set of common solutions to it, any ideas? </p>