Instructions to use hiroaki-f/binary_label_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiroaki-f/binary_label_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hiroaki-f/binary_label_classification") 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("hiroaki-f/binary_label_classification") model = AutoModelForImageClassification.from_pretrained("hiroaki-f/binary_label_classification") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- config.json +2 -2
- model.safetensors +1 -1
- training_args.bin +1 -1
config.json
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"No Pathology": 0,
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"No Pathology": "0",
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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model.safetensors
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training_args.bin
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