UperNet, Swin Transformer small sized backbone UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper Unified Perceptual Parsing for Scene Understanding by Xiao et al. Combining UperNet with a Swin Transformer backbone was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. 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. Intended uses & limitations You can use the raw model for semantic segmentation. See the model hub 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.
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