Instructions to use CIDAS/clipseg-rd64-refined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIDAS/clipseg-rd64-refined with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="CIDAS/clipseg-rd64-refined")# Load model directly from transformers import AutoProcessor, CLIPSegForImageSegmentation processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51f26d68971b92d0aed28e6ae2ea188620edf933a6924b87c71fc11c05a27822
- Size of remote file:
- 603 MB
- SHA256:
- dd9308225b8314bb7236f207e6ea72b22db5d90dba03fe3dc7d654f54dcfd08a
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