Instructions to use hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation 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-Data2VecVisionForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, Data2VecVisionForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation") model = Data2VecVisionForSemanticSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53e14ccd2d2efd20ac0e6ea7ca405bfe454ac34604e2531bd4113769dfc7ffa3
- Size of remote file:
- 1.14 MB
- SHA256:
- c5fe1a9a410acba298bbda3ee06a8ae2a15a96f9abfe2c2888089bbeee8447fd
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