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:
- 526b42aca3d20bedc5cec1ea6e4e4842df3a116b0cdf43bf7bc1b78a5ba5de8d
- Size of remote file:
- 86.9 MB
- SHA256:
- bd8d55d2a589305d9c767571080e674bae195cfd5e5bac2b608080f43f25b2c6
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