Instructions to use hf-tiny-model-private/tiny-random-Swinv2ForMaskedImageModeling 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-Swinv2ForMaskedImageModeling with Transformers:
# Load model directly from transformers import AutoImageProcessor, Swinv2ForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2ForMaskedImageModeling") model = Swinv2ForMaskedImageModeling.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2ForMaskedImageModeling") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e095d26ac7ca29441167c55d89fe1f7e3c7b3ad57cfcc5279f4d0c4f171d7476
- Size of remote file:
- 379 kB
- SHA256:
- c765793a89b95762cf77fddb7b6c2af21e2bb3dbc3fb90e2fa4a87ac911c5520
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