Instructions to use google/vit-large-patch32-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-large-patch32-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="google/vit-large-patch32-224-in21k")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-large-patch32-224-in21k") model = AutoModel.from_pretrained("google/vit-large-patch32-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dd51c19e7709493fda50b61d7a602c20d86ba24b997da1c33ba98d83104a680
- Size of remote file:
- 1.23 GB
- SHA256:
- 5facddf9114f99190742f0adff14c61ebfddd46b7f08b35f6001db74ed5d0d7a
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