readme: ---
  license: other
  tags:
  - vision
  - image-segmentation
  datasets:
  - scene_parse_150
  widget:
  - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
    example_title: House
  - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
    example_title: Castle
  ---
  
  # SegFormer (b4-sized) model fine-tuned on ADE20k
  
  SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). 
  
  Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Model description
  
  SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
  
  ## Intended uses & limitations
  
  You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
  
  ```python
  from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
  from PIL import Image
  import requests
  
  feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b4-finetuned-ade-512-512")
  model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b4-finetuned-ade-512-512")
  
  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
  ```
  
  For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).
  
  ### License
  
  The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/abs-2105-15203,
    author    = {Enze Xie and
                 Wenhai Wang and
                 Zhiding Yu and
                 Anima Anandkumar and
                 Jose M. Alvarez and
                 Ping Luo},
    title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
                 Transformers},
    journal   = {CoRR},
    volume    = {abs/2105.15203},
    year      = {2021},
    url       = {https://arxiv.org/abs/2105.15203},
    eprinttype = {arXiv},
    eprint    = {2105.15203},
    timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  