Upernet Swin Base

🚀 UperNet, Swin Transformer base-sized backbone

UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. It provides a powerful solution for pixel-level semantic labeling in images.

🚀 Quick Start

UperNet is a framework for semantic segmentation. It 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.

✨ Features

• Semantic Segmentation: UperNet is designed for semantic segmentation, predicting a semantic label per pixel.
• Modular Design: It consists of components like a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM). Any visual backbone can be plugged into the UperNet framework.

📚 Documentation

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.

📄 License

This project is licensed under the MIT license.