Instructions to use ppak10/test_ViT-Masked_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppak10/test_ViT-Masked_2 with Transformers:
# Load model directly from transformers import AutoImageProcessor, ViTForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("ppak10/test_ViT-Masked_2") model = ViTForMaskedImageModeling.from_pretrained("ppak10/test_ViT-Masked_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3738ea04303313483c1639b45aaf96cfbac9af30e88d2a83735aa31561977b71
- Size of remote file:
- 4.54 kB
- SHA256:
- ce9fb51477e154484dc633656ce0f2c7abb20938f802d0f7a39d22688d9f51b1
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