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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-base") model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (45943061aecdf39371286f29669dbbd322683b1b) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>13a9c41
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Upload README.md with huggingface_hub
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Upload processor
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Upload UperNetForSemanticSegmentation
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initial commit
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