Instructions to use hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation") model = UperNetForSemanticSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation: direct link, hf CLI and curl.
- Browser
- Download file 86.9 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-UperNetForSemanticSegmentation/resolve/refs%2Fpr%2F1/model.safetensors
86.9 MB
- Xet hash:
- 248ff1b669fcf6a6f6ca67247279e27521a10cb43e27fe93de749a095a86afae
- Size of remote file:
- 86.9 MB
- SHA256:
- 4e866fc4f9adfd5b769e1905d6e7a75c4889b3ab74d01f0cc710abb6c047ace3
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