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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14cef00259871c312131049294de6380d7d4942baa2bd70b28466a58f196a2ef
- Size of remote file:
- 86.9 MB
- SHA256:
- 99e95af90af8f91e453b28ff4eb5d73b9a90853324371d88a36b0b512625186d
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