Upernet Convnext Base

🚀 UperNet, ConvNeXt base-sized backbone

The UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone, offers an effective solution for pixel-level semantic labeling.

🚀 Quick Start

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, and the framework predicts a semantic label per pixel.

You can use the raw model for semantic segmentation. Check the model hub to find fine - tuned versions (with various backbones) for tasks that interest you.

✨ 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 ConvNeXt backbone was introduced in the paper A ConvNet for the 2020s.
• It can accept any visual backbone and predict semantic labels for each 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 model is released under the MIT license.