where one first trains a [~transformers.DetrForObjectDetection] model to detect bounding boxes around both
"things" (instances) and "stuff" (background things like trees, roads, sky), then freeze all the weights and train only
the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
Tips:
DETR uses so-called object queries to detect objects in an image. The number of queries determines the maximum
  number of objects that can be detected in a single image, and is set to 100 by default (see parameter
  num_queries of [~transformers.DetrConfig]). Note that it's good to have some slack (in COCO, the
  authors used 100, while the maximum number of objects in a COCO image is ~70).
