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+ ---
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+ language: en
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+ license: mit
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+ tags:
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+ - vision
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+ - image-segmentation
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+ model_name: openmmlab/upernet-swin-base
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+ ---
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+ # UperNet, Swin Transformer base-sized backbone
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+ 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.
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+ 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).
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+ 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.
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+ ## Model description
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+ 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).
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+ Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
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+ ![UperNet architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/upernet_architecture.jpg)
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+
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+ ## Intended uses & limitations
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+ You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
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+ fine-tuned versions (with various backbones) on a task that interests you.
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+ ### How to use
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+ For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).