Instructions to use google/vit-base-patch16-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-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-base-patch16-224-in21k")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") model = AutoModel.from_pretrained("google/vit-base-patch16-224-in21k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- add86b93b97489785302586d2b69b472b8ff1adb4ff71df59c1ec222c4d42ec1
- Size of remote file:
- 346 MB
- SHA256:
- 84066da0f5d8ff1cc494c660d4693141fae2e356535bf18a14d9fc00a055a6a1
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