Instructions to use google/vit-large-patch32-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-large-patch32-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-large-patch32-384") 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("google/vit-large-patch32-384") model = AutoModelForImageClassification.from_pretrained("google/vit-large-patch32-384") - Inference
- Notebooks
- Google Colab
- Kaggle
Update model
Browse files- config.json +1 -2
- pytorch_model.bin +1 -1
config.json
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"num_channels": 3,
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"num_hidden_layers": 24,
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"patch_size": 32,
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"transformers_version": "4.5.0.dev0"
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"use_pooler": false
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}
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"num_channels": 3,
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"num_hidden_layers": 24,
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"patch_size": 32,
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"transformers_version": "4.5.0.dev0"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:1558cf23dedc8c1bd7aed8e20b316e31b54669036dc864cbadb9bf6e5256e613
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size 1226690223
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