Instructions to use hf-internal-testing/tiny-random-UperNetForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UperNetForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, UperNetForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-UperNetForSemanticSegmentation") model = UperNetForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-UperNetForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e11ca0d9f35a89bc9843409ce3a6f9d9b85108cdaed7955eb46f8dc1081f435a
- Size of remote file:
- 86.9 MB
- SHA256:
- 6ea199d10f93b8c8abac2faf11fca3315dd568c5d67a1355e0721a7daf776554
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