Instructions to use CIDAS/clipseg-rd64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIDAS/clipseg-rd64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="CIDAS/clipseg-rd64")# Load model directly from transformers import AutoProcessor, CLIPSegForImageSegmentation processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c5a0b071ba28c155d7ba9c2f1100106bb9508f300254601c0a3e7149d5bb7c7
- Size of remote file:
- 603 MB
- SHA256:
- 63e3e34334d7ef9975b1ffcf17d78b3391e0556241ec9ac654f6c19248b6a5b7
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