Instructions to use tsushil/vit-base-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsushil/vit-base-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tsushil/vit-base-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("tsushil/vit-base-cifar10") model = AutoModelForImageClassification.from_pretrained("tsushil/vit-base-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7787e57e2ce4f30305e64cc9dbd7d580432a2c6173293321b603deec818a9148
- Size of remote file:
- 3.06 kB
- SHA256:
- cbe3582e8baaabc8a4c6bb27b7a3cbbe345e0d96c46ab1d67298c11bf5a140d3
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