Instructions to use Prot10/vit-base-patch16-224-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/vit-base-patch16-224-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/vit-base-patch16-224-for-pre_evaluation") 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("Prot10/vit-base-patch16-224-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/vit-base-patch16-224-for-pre_evaluation") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
config.json
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.33.1"
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}
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pytorch_model.bin
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training_args.bin
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