title: Best practice for TF Object Detection API for very large images
body: <p>The <a href="https://github.com/tensorflow/models/tree/master/research/object_detection" rel="nofollow noreferrer">Tensorflow Object Detection API</a> offers a <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md" rel="nofollow noreferrer">variety of models</a>.  These are trained at 600x600 image size.  Suppose I have a 6000x4000 satellite image, and I want to detect objects continuously throughout the image.  What is the best practice for adapting a TFODI model to this image size?  I don't care about the running time per image for object detection.  I have a GPU with 9GB of RAM. 
   I know I can fit a single 6000x4000 image onto this GPU.  I'm not sure if I can fit an image processing neural net for that size onto the GPU.  I can think of a few alternatives:</p>
  
  <ul>
  <li><p>Chip the image into 600x600 blocks, which risks losing features that cross the blocks, but then everything should work out of the box.</p></li>
  <li><p>Change the image dimensions in the model definition from 600x600 to 6000x4000.  Can I retrain from the Model Zoo checkpoint, or do I have to start from scratch if I do this?</p></li>
  <li><p>Compress the image to smaller size.  This distorts the image dimensions and also loses feature detail.  For say a picture of a city, the resulting detail would not be adequate to pick out cars and small houses.</p></li>
  </ul>