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transformers/docs/source/en/model_doc/segformer.md

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+*This model was released on 2021-05-31 and added to Hugging Face Transformers on 2021-10-28.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+           <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# 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
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(task="image-segmentation", model="nvidia/segformer-b0-finetuned-ade-512-512", torch_dtype=torch.float16)
+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 AutoProcessor, AutoModelForSemanticSegmentation
+
+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")
+
+inputs = processor(images=image, return_tensors="pt")
+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
+
+## SegformerFeatureExtractor
+
+[[autodoc]] SegformerFeatureExtractor
+    - __call__
+    - post_process_semantic_segmentation
+
+## SegformerImageProcessor
+
+[[autodoc]] SegformerImageProcessor
+    - preprocess
+    - post_process_semantic_segmentation
+
+## SegformerImageProcessorFast
+
+[[autodoc]] SegformerImageProcessorFast
+    - preprocess
+    - post_process_semantic_segmentation
+
+## SegformerModel
+
+[[autodoc]] SegformerModel
+    - forward
+
+## SegformerDecodeHead
+
+[[autodoc]] SegformerDecodeHead
+    - forward
+
+## SegformerForImageClassification
+
+[[autodoc]] SegformerForImageClassification
+    - forward
+
+## SegformerForSemanticSegmentation
+
+[[autodoc]] SegformerForSemanticSegmentation
+    - forward