Instructions to use nommis/nommis_upernet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nommis/nommis_upernet with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("nommis/nommis_upernet") model = UperNetForSemanticSegmentation.from_pretrained("nommis/nommis_upernet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nommis/nommis_upernet: direct link, hf CLI and curl.
- Browser
- Download file 241 MB
-
https://huggingface.co/nommis/nommis_upernet/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nommis/nommis_upernet/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nommis/nommis_upernet/resolve/main/pytorch_model.bin
241 MB
- Xet hash:
- 4c3e2bb50b4ac2775ea0533a1ca6bffd5b262aaf8e5f99bd19261244d2b431f6
- Size of remote file:
- 241 MB
- SHA256:
- c5850c98b8a8d2e4354b3f7f16c9b0e0ba1c29fbadf68a292cc7f1f97f05025f
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