Instructions to use google/vit-large-patch16-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-large-patch16-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="google/vit-large-patch16-224-in21k")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-large-patch16-224-in21k") model = AutoModel.from_pretrained("google/vit-large-patch16-224-in21k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f670a3b87d1cfa2212dc3b85d9163c8c2fd515f00a81f6a4a7e30bdd8739dca
- Size of remote file:
- 1.22 GB
- SHA256:
- 2610cfd406a0f431afddccce6466405f8094e978a8b483da5cb2022b7a498214
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