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README.md

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+---
+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).