Instructions to use jcbao77/google_vit-base-patch16-224-in21k_image_classification_5 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_5 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_5") 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_5") model = AutoModelForImageClassification.from_pretrained("jcbao77/google_vit-base-patch16-224-in21k_image_classification_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 330f97c16bdc9d2a7aa82cd3c317654e72a2a88b645db70265aa3de76aa853d2
- Size of remote file:
- 45.2 MB
- SHA256:
- a89c1b9e4dc5fa8cd87af8dfa61025d2f8fef34cc7654b1a9742f367af611506
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