Instructions to use Borjamg/pneumonia_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Borjamg/pneumonia_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Borjamg/pneumonia_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Borjamg/pneumonia_model") model = AutoModelForImageClassification.from_pretrained("Borjamg/pneumonia_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This model identifies pneumonia in chest x-ray images
In the "Inference API" interface on this page:
- LABEL_0 is a NORMAL result
- LABEL_1 is a positive result for PNEUMONIA
*Model Architecture: This model is based on Google’s ViT (Vision Transformer), fine-tuned for image classification with two labels. The original ViT model was implemented using the google/vit-base-patch16-224 architecture from Hugging Face's Transformers library.
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