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
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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@@ -3,6 +3,7 @@ license: apache-2.0
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tags:
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- image-classification
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- generated_from_trainer
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model-index:
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- name: vit-base-cifar10
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results: []
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tags:
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- image-classification
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- generated_from_trainer
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base_model: nateraw/vit-base-patch16-224-cifar10
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model-index:
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- name: vit-base-cifar10
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results: []
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