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:
# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 248ff1b669fcf6a6f6ca67247279e27521a10cb43e27fe93de749a095a86afae
- Size of remote file:
- 86.9 MB
- SHA256:
- 4e866fc4f9adfd5b769e1905d6e7a75c4889b3ab74d01f0cc710abb6c047ace3
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