In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
[~transformers.DetrFeatureExtractor] to create pixel_values, pixel_mask and optional
labels, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
outputs of the model using one of the postprocessing methods of [~transformers.DetrFeatureExtractor]. These can
be be provided to either CocoEvaluator or PanopticEvaluator, which allow you to calculate metrics like
mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the original repository. See the example notebooks for more info regarding evaluation.
DETR specific outputs
[[autodoc]] models.detr.modeling_detr.DetrModelOutput
[[autodoc]] models.detr.modeling_detr.DetrObjectDetectionOutput
