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*This model was published in HF papers on 2021-05-31 and contributed to Hugging Face Transformers on 2021-10-28.*


# SegFormer

[SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://huggingface.co/papers/2105.15203) is a semantic segmentation model that combines a hierarchical Transformer encoder (Mix Transformer, MiT) with a lightweight all-MLP decoder. It avoids positional encodings and complex decoders and achieves state-of-the-art performance on benchmarks like ADE20K and Cityscapes. This simple and lightweight design is more efficient and scalable.

The figure below illustrates the architecture of SegFormer.

<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png"/>

You can find all the original SegFormer checkpoints under the [NVIDIA](https://huggingface.co/nvidia/models?search=segformer) organization.

> [!TIP]
> This model was contributed by [nielsr](https://huggingface.co/nielsr).
>
> Click on the SegFormer models in the right sidebar for more examples of how to apply SegFormer to different vision tasks.

The example below demonstrates semantic segmentation with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(task="image-segmentation", model="nvidia/segformer-b0-finetuned-ade-512-512")
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
```

</hfoption>
<hfoption id="AutoModel">

```python
import requests
from PIL import Image

from transformers import AutoModelForSemanticSegmentation, AutoProcessor


url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = AutoModelForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512", device_map="auto")

inputs = processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)
logits = outputs.logits # shape [batch, num_labels, height, width]
```

</hfoption>

</hfoptions>

## Notes

- SegFormer works with **any input size**, padding inputs to be divisible by `config.patch_sizes`.
- The most important preprocessing step is to randomly crop and pad all images to the same size (such as 512x512 or 640x640) and normalize afterwards.
- Some datasets (ADE20k) uses the `0` index in the annotated segmentation as the background, but doesn't include the "background" class in its labels. The `do_reduce_labels` argument in [`SegformerForImageProcessor`] is used to reduce all labels by `1`. To make sure no loss is computed for the background class, it replaces `0` in the annotated maps by `255`, which is the `ignore_index` of the loss function.

   Other datasets may include a background class and label though, in which case, `do_reduce_labels` should be `False`.

```python
from transformers import SegformerImageProcessor


processor = SegformerImageProcessor(do_reduce_labels=True)
```

## Resources

- [Original SegFormer code (NVlabs)](https://github.com/NVlabs/SegFormer)  
- [Fine-tuning blog post](https://huggingface.co/blog/fine-tune-segformer)  
- [Tutorial notebooks (Niels Rogge)](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer)  
- [Hugging Face demo space](https://huggingface.co/spaces/chansung/segformer-tf-transformers)  

## SegformerConfig

[[autodoc]] SegformerConfig

## SegformerImageProcessor

[[autodoc]] SegformerImageProcessor
    - preprocess
    - post_process_semantic_segmentation

## SegformerImageProcessorPil

[[autodoc]] SegformerImageProcessorPil
    - preprocess
    - post_process_semantic_segmentation

## SegformerModel

[[autodoc]] SegformerModel
    - forward

## SegformerDecodeHead

[[autodoc]] SegformerDecodeHead
    - forward

## SegformerForImageClassification

[[autodoc]] SegformerForImageClassification
    - forward

## SegformerForSemanticSegmentation

[[autodoc]] SegformerForSemanticSegmentation
    - forward
