Instructions to use CIDAS/clipseg-rd16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIDAS/clipseg-rd16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="CIDAS/clipseg-rd16")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, CLIPSegForImageSegmentation processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd16") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CIDAS/clipseg-rd16: direct link, hf CLI and curl.
- Browser
- Download file 600 MB
-
https://huggingface.co/CIDAS/clipseg-rd16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CIDAS/clipseg-rd16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CIDAS/clipseg-rd16/resolve/main/pytorch_model.bin
600 MB
- Xet hash:
- 1dd45eae35b653610e33e974c51ddb5d1f169f12c594cb6279a637bcd60fbda2
- Size of remote file:
- 600 MB
- SHA256:
- 5718c1512818a227bff925e22d5c57e68e691d4565884052b718e9b08c07a0b0
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