From 550b68d291f9a7e4874065c6eec0676b2ba821e6 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 19 Jan 2023 10:45:20 +0000 Subject: [PATCH] Upload README.md with huggingface_hub --- README.md | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..6662a7c --- /dev/null +++ b/README.md @@ -0,0 +1,33 @@ +--- +language: en +license: mit +tags: +- vision +- image-segmentation +model_name: openmmlab/upernet-convnext-small +--- + +# 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](https://arxiv.org/abs/1807.10221) by Xiao et al. + +Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545). + +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. + +![UperNet architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/upernet_architecture.jpg) + +## Intended uses & limitations + +You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) 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](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).