  at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
  least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
  [~transformers.DetrFeatureExtractor] to prepare images (and optional annotations in COCO format) for the
  model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
  largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
  Alternatively, one can also define a custom collate_fn in order to batch images together, using
  [~transformers.DetrFeatureExtractor.pad_and_create_pixel_mask].
The size of the images will determine the amount of memory being used, and will thus determine the batch_size.
