Instructions to use simlaharma/vit-base-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simlaharma/vit-base-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="simlaharma/vit-base-cifar10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("simlaharma/vit-base-cifar10") model = AutoModelForImageClassification.from_pretrained("simlaharma/vit-base-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from simlaharma/vit-base-cifar10: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/simlaharma/vit-base-cifar10/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://simlaharma/vit-base-cifar10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/simlaharma/vit-base-cifar10/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- a74b1e99553594cbc2d054474ea3a035cbc0a0e9f4313a21c73ec13b5255507c
- Size of remote file:
- 343 MB
- SHA256:
- 94d7a61ac4b580a5f2fc62c8c628f44564a36611c0268d157916d61cf0d24a98
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