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
- Xet hash:
- fbc3f8bb9d6dcbbfd1cdda454ddde6b8027fafebc2245a8e0a5891d8fa003b04
- Size of remote file:
- 489 MB
- SHA256:
- 9e18ac00beff2cb9e1e1c727250fda74cd6525dcb0357c3e6f5fa0a78a336c9d
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