Image Classification
Transformers
PyTorch
Safetensors
efficientnet
chest-xray
efficientnet-b0
medical-ai
radiology
deep-learning
Eval Results (legacy)
Instructions to use Dragonscypher/rayz_EfficientNet_B0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dragonscypher/rayz_EfficientNet_B0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dragonscypher/rayz_EfficientNet_B0") 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("Dragonscypher/rayz_EfficientNet_B0") model = AutoModelForImageClassification.from_pretrained("Dragonscypher/rayz_EfficientNet_B0") - Notebooks
- Google Colab
- Kaggle
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datasets:
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- nih-chest-xray
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- nlmcxr
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library_name: transformers
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datasets:
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- nih-chest-xray
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- nlmcxr
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