Instructions to use TornikeO/vit-base-patch16-224-imigue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TornikeO/vit-base-patch16-224-imigue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TornikeO/vit-base-patch16-224-imigue") 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("TornikeO/vit-base-patch16-224-imigue") model = AutoModelForImageClassification.from_pretrained("TornikeO/vit-base-patch16-224-imigue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3d58c38cd99677503819292f000ecb203a5ff968cd63b939991aa055d9982eb
- Size of remote file:
- 4.92 kB
- SHA256:
- a16c754cf4c207d2ba7eb9186adc2fda72184fb8768121adf5532b8b67d0f185
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