Upernet Convnext Large

🚀 UperNet, ConvNeXt large-sized backbone

This project presents the UperNet framework for semantic segmentation, which utilizes a ConvNeXt backbone. It effectively addresses the challenges in semantic segmentation tasks, offering high - precision pixel - level semantic labeling.

🚀 Quick Start

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

✨ Features

• Flexible Backbone Integration: Any visual backbone can be plugged into the UperNet framework.
• Precise Pixel - Level Prediction: The framework predicts 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).

The concept of UperNet was introduced in the paper Unified Perceptual Parsing for Scene Understanding by Xiao et al. Combining UperNet with a ConvNeXt backbone was introduced in the paper A ConvNet for the 2020s.

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.