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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, CLIPSegForImageSegmentation processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from CIDAS/clipseg-rd64: direct link, hf CLI and curl.
- Browser
- Download file 603 MB
-
https://huggingface.co/CIDAS/clipseg-rd64/resolve/main/model.safetensors
- Command line
-
hf download hf://CIDAS/clipseg-rd64/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/CIDAS/clipseg-rd64/resolve/main/model.safetensors
603 MB
- Xet hash:
- 1941dcffe4aa4545190678cf285df0e6df5344b9e6c2d06dcac13a7eb1cb7116
- Size of remote file:
- 603 MB
- SHA256:
- 2e166d09082e13335e00b78660ceb6dc2aeeb099619ca8a8f79320c3e56ea13c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.