Instructions to use MatanBT/vit-base-patch16-224-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatanBT/vit-base-patch16-224-cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatanBT/vit-base-patch16-224-cifar100") 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-cifar100") model = AutoModelForImageClassification.from_pretrained("MatanBT/vit-base-patch16-224-cifar100") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45d7427018c3ec8fea02df001da4f51ee7bc109bc83d9e1de17115c4910a9af3
- Size of remote file:
- 4.86 kB
- SHA256:
- 8479e07a3636231c1e84bfc9f3a3bdd98b7b9a3e8a681f4f54151975c138c346
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