Instructions to use MatanBT/vit-base-patch16-224-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatanBT/vit-base-patch16-224-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatanBT/vit-base-patch16-224-cifar10") 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("MatanBT/vit-base-patch16-224-cifar10") model = AutoModelForImageClassification.from_pretrained("MatanBT/vit-base-patch16-224-cifar10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e22016d4c79e17546cd1a26fde9022b0a4b22e890946a3517b1f0828e993b9b
- Size of remote file:
- 4.86 kB
- SHA256:
- a86e9599d15ab90e36a1d8cfdc7b5b7577515c1db8a5775a93fe86dcbef87a0f
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