  found on the hub.
The quickest way to get started with SegFormer is by checking the example notebooks (which showcase both inference and
  fine-tuning on custom data).
One can use [SegformerFeatureExtractor] to prepare images and corresponding segmentation maps
  for the model. Note that this feature extractor is fairly basic and does not include all data augmentations used in
  the original paper. The original preprocessing pipelines (for the ADE20k dataset for instance) can be found here. The most
  important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
  such as 512x512 or 640x640, after which they are normalized.
One additional thing to keep in mind is that one can initialize [SegformerFeatureExtractor] with
  reduce_labels set to True or False. In some datasets (like ADE20k), the 0 index is used in the annotated
