Instructions to use jcbao77/google_vit-base-patch16-224-in21k_image_classification_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcbao77/google_vit-base-patch16-224-in21k_image_classification_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jcbao77/google_vit-base-patch16-224-in21k_image_classification_0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("jcbao77/google_vit-base-patch16-224-in21k_image_classification_0") model = AutoModelForImageClassification.from_pretrained("jcbao77/google_vit-base-patch16-224-in21k_image_classification_0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a67acfa9e670bdddd10bcaf7e9e03338a241fe0731d029f7bf938ea8ad813053
- Size of remote file:
- 45.2 MB
- SHA256:
- 484838be8db494da95cd8c91439d3d58458cfc6e33ea72d6ac497553731dd7eb
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