79 lines
4.2 KiB
Markdown
79 lines
4.2 KiB
Markdown
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# UPerNet
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## Overview
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The UPerNet model was proposed in [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221)
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by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
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a wide range of concepts from images, leveraging any vision backbone like [ConvNeXt](convnext) or [Swin](swin).
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The abstract from the paper is the following:
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*Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/upernet_architecture.jpg"
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alt="drawing" width="600"/>
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<small> UPerNet framework. Taken from the <a href="https://arxiv.org/abs/1807.10221">original paper</a>. </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code is based on OpenMMLab's mmsegmentation [here](https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/uper_head.py).
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## Usage examples
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UPerNet is a general framework for semantic segmentation. It can be used with any vision backbone, like so:
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```py
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from transformers import SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
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backbone_config = SwinConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
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config = UperNetConfig(backbone_config=backbone_config)
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model = UperNetForSemanticSegmentation(config)
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```
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To use another vision backbone, like [ConvNeXt](convnext), simply instantiate the model with the appropriate backbone:
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```py
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from transformers import ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation
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backbone_config = ConvNextConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
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config = UperNetConfig(backbone_config=backbone_config)
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model = UperNetForSemanticSegmentation(config)
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```
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Note that this will randomly initialize all the weights of the model.
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## Resources
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A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with UPerNet.
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- Demo notebooks for UPerNet can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/UPerNet).
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- [`UperNetForSemanticSegmentation`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/semantic-segmentation) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb).
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- See also: [Semantic segmentation task guide](../tasks/semantic_segmentation)
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If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
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## UperNetConfig
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[[autodoc]] UperNetConfig
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## UperNetForSemanticSegmentation
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[[autodoc]] UperNetForSemanticSegmentation
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- forward |