readme: ---
  language: en
  license: mit
  tags:
  - vision
  - image-segmentation
  model_name: openmmlab/upernet-swin-tiny
  ---
  
  # UperNet, Swin Transformer tiny-sized backbone
  
  UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
  
  Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030).
  
  Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
  
  ## Model description
  
  UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
  
  Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
  
  ![UperNet architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/upernet_architecture.jpg)
  
  ## Intended uses & limitations
  
  You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
  fine-tuned versions (with various backbones) on a task that interests you.
  
  ### How to use
  
  For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
  