Downloads · 30 days
12.7K
1% of all-time downloads
upernet-convnext-small
openmmlab/upernet-convnext-small
upernet-convnext-small is a image segmentation model from openmmlab. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper Unified Perceptual Parsing for Scene Understanding by Xiao et al.
All-time downloads
1.8M
Public
Repo size
655 MB
Likes
34
Public
Repo makeup
Click a slice to open those files.
.bin328 MB · 100%
At a glance
- Task
- Image Segmentation
- Library
- transformers
- License
- mit
- Model type
- upernet
- Access
- Public
- Created
- Jan 13, 2023
- Updated
- Jan 19, 2023
- SHA
- 550b68d2
Model card
From the Hugging Face model README
UperNet, ConvNeXt small-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. 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.
Disclaimer: The team releasing UperNet + ConvNeXt 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.
- Task
- Image Segmentation
- Library
- transformers
- Type
- upernet
- License
- mit
- Languages
- en
- Hf Inference
- live
- Created
- Jan 13, 2023
- Updated
- Jan 19, 2023