Upernet Swin Tiny

🚀 UperNet, Swin Transformer tiny-sized backbone

UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone, offering high - precision scene understanding.

🚀 Quick Start

You can use the raw model for semantic segmentation. Check out the model hub to find fine - tuned versions (with various backbones) for tasks that interest you. For code examples, refer to the documentation.

✨ Features

• UperNet is a semantic segmentation framework 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.
• It consists of a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM), and can accept any visual backbone, predicting a semantic label per pixel.

📚 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.