Instructions to use Scherzando/vit-cifar10-6ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scherzando/vit-cifar10-6ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Scherzando/vit-cifar10-6ep") 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("Scherzando/vit-cifar10-6ep") model = AutoModelForImageClassification.from_pretrained("Scherzando/vit-cifar10-6ep") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe7cbecaa121ab711286ead12e1c7d731b7f42c4f414d10f8d2152e85a33885b
- Size of remote file:
- 343 MB
- SHA256:
- 89f737b589d4b9d1cb143c180bf34d256f0285505208902b8fd3c104dcf44d44
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