text: Title: Is resizing of the input image done in both training and predicting?
  Body: In the hyperparameters, the standard settings for input image resizing are as below:
  
  
  `IMAGE_RESIZE_MODE = "square"`
  
  `IMAGE_MIN_DIM = 800`
  
  `IMAGE_MAX_DIM = 1024`
  
  I have input images that are 6080x3420, so to my understanding, these are resized to 1024x1024 and padded with zeroes to make a square image. Does this happen both in training and when predicting with the trained model?
  
  I ask because I have a model trained on the 6080x3420 images with the above standard settings, but I have noticed that downscaling the test images before predicting has an influence on prediction accuracy. Effectively, the prediction accuracy is highest when downscaling the test images to 12.5% of the original size before running the model on them.
  