Instructions to use Scherzando/vit-cifar10-ft-2ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scherzando/vit-cifar10-ft-2ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Scherzando/vit-cifar10-ft-2ep") 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-2ep") model = AutoModelForImageClassification.from_pretrained("Scherzando/vit-cifar10-ft-2ep") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84efd5757313f016790f143eb130f8ea40848604bf3fea5646d989c514301540
- Size of remote file:
- 343 MB
- SHA256:
- e7f2bde02053966f9252c833e7bff309341b60f547f8909c0c37dc569eba8812
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