Instructions to use hf-tiny-model-private/tiny-random-Data2VecVisionModel 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-Data2VecVisionModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-Data2VecVisionModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80bc5760a405214b6060b9e776b6424823f42cc5ccb4df955e2ab5b029911435
- Size of remote file:
- 208 kB
- SHA256:
- 8631ce9a98d95c1c32d80786e6db8b5c0431346fa15c3d1d701b13702b68d03b
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