title: CNN Object Localization Preprocessing?
body: <p>I'm trying to use a pretrained VGG16 as an object localizer in Tensorflow on ImageNet data. In their paper, the group mentions that they basically just strip off the softmax layer and either toss on a 4D/4000D fc layer for bounding box regression. I'm not trying to do anything fancy here (sliding windows, RCNN), just get some mediocre results.</p>
  
  <p>I'm sort of new to this and I'm just confused about the preprocessing done here for localization. In the paper, they say that they scale the image to 256 as its shortest side, then take the central 224x224 crop and train on this. I've looked all over and can't find a simple explanation on how to handle localization data.</p>
  
  <p>Questions: How do people usually handle the bounding boxes here?... </p>
  
  <ul>
  <li>Do you use something like the tf.sample_distorted_bounding_box command, and then rescale the image based on that?</li>
  <li>Do you just rescale/crop the image itself, and then interpolate the bounding box with the transformed scales? Wouldn't this result in negative box coordinates in some cases?</li>
  <li><p>How are multiple objects per image handled?</p>
  
  <ul>
  <li><p>Do you just choose a single bounding box from the beginning ,crop to that, then train on this crop?</p></li>
  <li><p>Or, do you feed it the whole (centrally cropped) image, and then try to predict 1 or more boxes somehow?</p></li>
  </ul></li>
  <li>Does any of this generalize to the Detection or segmentation (like MS-CoCo) challenges, or is it completely different?</li>
  </ul>
  
  <p>Anything helps...
  Thanks</p>