title: semantic segmentation for large images
body: <p>I am working on a limited number of large size images, each of which can have <code>3072*3072</code> pixels. To train a semantic segmentation model using FCN or U-net, I construct a large sample of training sets, each training image is <code>128*128</code>. </p>
  
  <p>In the prediction stage, what I do is to cut a large image into small pieces, the same as trainning set of <code>128*128</code>, and feed these small pieces into the trained model, get the predicted mask. Afterwards, I just stitch these small patches together to get the mask for the whole image. Is this the right mechanism to perform the semantic segmentation against the large images?</p>