# 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).

This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version

of the model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). The original code can be found [here](https://github.com/NVlabs/SegFormer).

## Usage tips

- SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decoder 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 decoder 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 also check out the [blog post](https://huggingface.co/blog/fine-tune-segformer) introducing SegFormer and illustrating how it can be fine-tuned on custom data.

- TensorFlow users should refer to [this repository](https://github.com/deep-diver/segformer-tf-transformers) that shows off-the-shelf inference and fine-tuning.

- One can also check out [this interactive demo on Hugging Face Spaces](https://huggingface.co/spaces/chansung/segformer-tf-transformers)

  to try out a SegFormer model on custom images.

- SegFormer works on any input size, as it pads the input to be divisible by `config.patch_sizes`.

- One can use [`SegformerImageProcessor`] to prepare images and corresponding segmentation maps

  for the model. Note that this image processor 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 [`SegformerImageProcessor`] 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

  (taken from Table 7 of the [original paper](https://arxiv.org/abs/2105.15203)).

| **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                  |

Note that MiT in the above table refers to the Mix Transformer encoder backbone introduced in SegFormer. For

SegFormer's results on the segmentation datasets like ADE20k, refer to the [paper](https://arxiv.org/abs/2105.15203).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SegFormer.

- [`SegformerForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).

- [Image classification task guide](../tasks/image_classification)

Semantic segmentation:

- [`SegformerForSemanticSegmentation`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/semantic-segmentation).

- A blog on fine-tuning SegFormer on a custom dataset can be found [here](https://huggingface.co/blog/fine-tune-segformer).

- More demo notebooks on SegFormer (both inference + fine-tuning on a custom dataset) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer).

- [`TFSegformerForSemanticSegmentation`] is supported by this [example notebook](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb).

- [Semantic segmentation task guide](../tasks/semantic_segmentation)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## SegformerConfig

[[autodoc]] SegformerConfig

## SegformerFeatureExtractor

[[autodoc]] SegformerFeatureExtractor

    - __call__

    - post_process_semantic_segmentation

## SegformerImageProcessor

[[autodoc]] SegformerImageProcessor

    - preprocess

    - post_process_semantic_segmentation

## SegformerModel

[[autodoc]] SegformerModel

    - forward

## SegformerDecodeHead

[[autodoc]] SegformerDecodeHead

    - forward

## SegformerForImageClassification

[[autodoc]] SegformerForImageClassification

    - forward

## SegformerForSemanticSegmentation

[[autodoc]] SegformerForSemanticSegmentation

    - forward

## TFSegformerDecodeHead

[[autodoc]] TFSegformerDecodeHead

    - call

## TFSegformerModel

[[autodoc]] TFSegformerModel

    - call

## TFSegformerForImageClassification

[[autodoc]] TFSegformerForImageClassification

    - call

## TFSegformerForSemanticSegmentation

[[autodoc]] TFSegformerForSemanticSegmentation

    - call