34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
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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-convnext-small
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---
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# UperNet, ConvNeXt small-sized backbone
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UperNet framework for semantic segmentation, leveraging a ConvNeXt 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 ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545).
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Disclaimer: The team releasing UperNet + ConvNeXt 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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## 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).
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