title: High-resolution image classification
body: <p>Usually pre-trained networks like VGG16 / Inception etc. works with low resolution like &lt; 500px. </p>
  
  <p>Is it possible to add a high-resolution convolution layer (or two) before the very first layer of <strong>pre-trained</strong> VGG16 / Inception to make the network be able to consume high-resolution pictures?</p>
  
  <p>As far as I know - the first layers are hardest to train, it took a lot of data and resources to train it. </p>
  
  <p>I wonder if it will be possible to freeze pre-trained network and train only the newly attached high-resolution layer on an average GPU card and about 3000 examples? Could it be done in a couple of hours?</p>
  
  <p>Also if you know any examples how to use high-resolution images for image classification please share the link. </p>
  
  <p>P.S. </p>
  
  <p>The problem with usual downscaling approach is that in our case the tiny details like tiny cracks or tiny dirt dots are very important and they are lost on lower-resolution images.</p>