Instructions to use nateraw/vit-base-patch16-224-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-patch16-224-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/vit-base-patch16-224-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("nateraw/vit-base-patch16-224-cifar10") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-patch16-224-cifar10", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3b4a367c905212b7c323612a37e26ae71fb020d7881ccd8df9b0d336cefb0a6
- Size of remote file:
- 343 MB
- SHA256:
- 5888ea39698a0fe8bb8ddeb8b41aa0d62e26585bcfc119948be1999d1fc49698
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.