Instructions to use fancifulcrow/deit-tiny-patch16-224-finetuned-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fancifulcrow/deit-tiny-patch16-224-finetuned-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fancifulcrow/deit-tiny-patch16-224-finetuned-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("fancifulcrow/deit-tiny-patch16-224-finetuned-cifar10") model = AutoModelForImageClassification.from_pretrained("fancifulcrow/deit-tiny-patch16-224-finetuned-cifar10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a01e04be4757d90c92827472ff0179905f1066dd29b764336a5dd81d7b78cd6
- Size of remote file:
- 22.1 MB
- SHA256:
- e26483426cdce1aa43f81ddfa4b7c14f67bbcd8306bafd74c71827933eb94d3a
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