Instructions to use hf-tiny-model-private/tiny-random-Swinv2Model 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-Swinv2Model 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-Swinv2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c829c8dc0e9f51eafc1c0c79bc1bb4326b4b50935934759e62a1e23c5cf5163c
- Size of remote file:
- 309 kB
- SHA256:
- f65c147296fcd910f355a4fef97903a8512cf436125b15ed6bafe5b651ade043
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