Instructions to use openmmlab/upernet-convnext-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openmmlab/upernet-convnext-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="openmmlab/upernet-convnext-base")# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-base") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-base") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f6df328bee3434ec3ed0ea14dc2288290e641173423c05682f2b923f9f2bb8cb
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size 488514240
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