Instructions to use toolevalxm/RadiologyVisionNet-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/RadiologyVisionNet-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/RadiologyVisionNet-TestRepo") 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("toolevalxm/RadiologyVisionNet-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/RadiologyVisionNet-TestRepo") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +7 -0
config.json
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{
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"model_type": "vit",
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"architectures": ["ViTForImageClassification"],
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"image_size": 224,
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"num_channels": 1,
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"num_labels": 14
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}
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