Instructions to use google/vit-base-patch32-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch32-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-base-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-base-patch32-384") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch32-384") - Inference
- Notebooks
- Google Colab
- Kaggle
add model
Browse files- config.json +1 -1
- flax_model.msgpack +3 -0
config.json
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 32,
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"transformers_version": "4.
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}
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 32,
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"transformers_version": "4.7.0.dev0"
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}
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:ed234aa90d5e6d065f16b5882cfb0cdab1dd8d8d4ab13036f4f0fe87b929bef7
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size 353196137
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