Instructions to use tzhao3/vit-test-CIFAR10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-test-CIFAR10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-test-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("tzhao3/vit-test-CIFAR10") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-test-CIFAR10") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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version https://git-lfs.github.com/spec/v1
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oid sha256:8721bcf6f94f8d79d45c5ce76ddda520d5d79dae6851d673ffa42ae160f024a9
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size 343248584
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