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docs/source/model_doc/segformer.mdx

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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+
+# SegFormer
+
+## Overview
+
+The SegFormer model was proposed in [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping
+Luo. The model consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great
+results on image segmentation benchmarks such as ADE20K and Cityscapes.
+
+The abstract from the paper is the following:
+
+*We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with
+lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel
+hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding,
+thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution
+differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from
+different layers, and thus combining both local attention and global attention to render powerful representations. We
+show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our
+approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance
+and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters,
+being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on
+Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C.*
+
+The figure below illustrates the architecture of SegFormer. Taken from the [original paper](https://arxiv.org/abs/2105.15203).
+
+<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png"/>
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/NVlabs/SegFormer).
+
+Tips:
+
+- SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decode head.
+  [`SegformerModel`] is the hierarchical Transformer encoder (which in the paper is also referred to
+  as Mix Transformer or MiT). [`SegformerForSemanticSegmentation`] adds the all-MLP decode head on
+  top to perform semantic segmentation of images. In addition, there's
+  [`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 [`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 [`SegformerFeatureExtractor`] with
+  `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, `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 [`SegformerForSemanticSegmentation`]). However, other datasets use the 0 index as
+  background class and include this class as part of all labels. In that case, `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
+
+[[autodoc]] SegformerConfig
+
+## SegformerFeatureExtractor
+
+[[autodoc]] SegformerFeatureExtractor
+    - __call__
+
+## SegformerModel
+
+[[autodoc]] SegformerModel
+    - forward
+
+## SegformerDecodeHead
+
+[[autodoc]] SegformerDecodeHead
+    - forward
+
+## SegformerForImageClassification
+
+[[autodoc]] SegformerForImageClassification
+    - forward
+
+## SegformerForSemanticSegmentation
+
+[[autodoc]] SegformerForSemanticSegmentation
+    - forward