Instructions to use Scherzando/vit-cifar10-ft-1ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scherzando/vit-cifar10-ft-1ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Scherzando/vit-cifar10-ft-1ep") 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-ft-1ep") model = AutoModelForImageClassification.from_pretrained("Scherzando/vit-cifar10-ft-1ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c22f38575b245c384719d2fca302552351756d1215537967f02ce801740a7b53
- Size of remote file:
- 343 MB
- SHA256:
- 9cba2a257d5c53ee17a80d923345dcfaa87d9525405fba685d628923c1dea772
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