
UPerNet
Overview
The UPerNet model was proposed in Unified Perceptual Parsing for Scene Understanding
by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
a wide range of concepts from images, leveraging any vision backbone like ConvNeXt or Swin.
The abstract from the paper is the following:
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.

 UPerNet framework. Taken from the original paper. 
This model was contributed by nielsr. The original code is based on OpenMMLab's mmsegmentation here.
Usage examples
UPerNet is a general framework for semantic segmentation. It can be used with any vision backbone, like so:

from transformers import SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
backbone_config = SwinConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)

To use another vision backbone, like ConvNeXt, simply instantiate the model with the appropriate backbone:

from transformers import ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation
backbone_config = ConvNextConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)

Note that this will randomly initialize all the weights of the model.
Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with UPerNet.

Demo notebooks for UPerNet can be found here.
[UperNetForSemanticSegmentation] is supported by this example script and notebook.
See also: Semantic segmentation task guide

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.
UperNetConfig
[[autodoc]] UperNetConfig
UperNetForSemanticSegmentation
[[autodoc]] UperNetForSemanticSegmentation
    - forward