Improve semantic segmentation models (#14355)

* Improve tests

* Improve documentation

* Add ignore_index attribute

* Add semantic_ignore_index to BEiT model

* Add segmentation maps argument to BEiTFeatureExtractor

* Simplify SegformerFeatureExtractor and corresponding tests

* Improve tests

* Apply suggestions from code review

* Minor docs improvements

* Streamline segmentation map tests of SegFormer and BEiT

* Improve reduce_labels docs and test

* Fix code quality

* Fix code quality again

docs/source/model_doc/segformer.rst

@@ -38,6 +38,58 @@ Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes
 This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
 <https://github.com/NVlabs/SegFormer>`__.
 
+The figure below illustrates the architecture of SegFormer. Taken from the `original paper
+<https://arxiv.org/abs/2105.15203>`__.
+
+.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png
+  :width: 600
+
+Tips:
+
+- SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decode head.
+  :class:`~transformers.SegformerModel` is the hierarchical Transformer encoder (which in the paper is also referred to
+  as Mix Transformer or MiT). :class:`~transformers.SegformerForSemanticSegmentation` adds the all-MLP decode head on
+  top to perform semantic segmentation of images. In addition, there's
+  :class:`~transformers.SegformerForImageClassification` which can be used to - you guessed it - classify images. The
+  authors of SegFormer first pre-trained the Transformer encoder on ImageNet-1k to classify images. Next, they throw
+  away the classification head, and replace it by the all-MLP decode head. Next, they fine-tune the model altogether on
+  ADE20K, Cityscapes and COCO-stuff, which are important benchmarks for semantic segmentation. All checkpoints can be
+  found on the `hub <https://huggingface.co/models?other=segformer>`__.
+- The quickest way to get started with SegFormer is by checking the `example notebooks
+  <https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer>`__ (which showcase both inference and
+  fine-tuning on custom data).
+- One can use :class:`~transformers.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
+  <https://github.com/NVlabs/SegFormer/blob/master/local_configs/_base_/datasets/ade20k_repeat.py>`__. 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 :class:`~transformers.SegformerFeatureExtractor` with
+  :obj:`reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
+  segmentation maps for background. However, ADE20k doesn't include the "background" class in its 150 labels.
+  Therefore, :obj:`reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
+  background class (i.e. it replaces 0 in the annotated maps by 255, which is the `ignore_index` of the loss function
+  used by :class:`~transformers.SegformerForSemanticSegmentation`). However, other datasets use the 0 index as
+  background class and include this class as part of all labels. In that case, :obj:`reduce_labels` should be set to
+  `False`, as loss should also be computed for the background class.
+- As most models, SegFormer comes in different sizes, the details of which can be found in the table below.
+
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| **Model variant** | **Depths**    | **Hidden sizes**    | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b0            | [2, 2, 2, 2]  | [32, 64, 160, 256]  | 256                     | 3.7            | 70.5                  |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b1            | [2, 2, 2, 2]  | [64, 128, 320, 512] | 256                     | 14.0           | 78.7                  |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b2            | [3, 4, 6, 3]  | [64, 128, 320, 512] | 768                     | 25.4           | 81.6                  |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b3            | [3, 4, 18, 3] | [64, 128, 320, 512] | 768                     | 45.2           | 83.1                  |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b4            | [3, 8, 27, 3] | [64, 128, 320, 512] | 768                     | 62.6           | 83.6                  |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b5            | [3, 6, 40, 3] | [64, 128, 320, 512] | 768                     | 82.0           | 83.8                  |
++-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+
 SegformerConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~