Image Classification
CXR Foundation
TF-Keras
English
medical
x-ray
chest-x-ray
medical-embeddings
zero-shot-image-classification
image-feature-extraction
image-text-to-text
visual-question-answering
Instructions to use google/cxr-foundation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- CXR Foundation
How to use google/cxr-foundation with CXR Foundation:
# pip install git+https://github.com/Google-Health/cxr-foundation.git#subdirectory=python # Load image as grayscale (Stillwaterising, CC0, via Wikimedia Commons) import requests from PIL import Image from io import BytesIO image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png" img = Image.open(requests.get(image_url, headers={'User-Agent': 'Demo'}, stream=True).raw).convert('L') # Run inference from clientside.clients import make_hugging_face_client cxr_client = make_hugging_face_client('cxr_model') print(cxr_client.get_image_embeddings_from_images([img])) - Notebooks
- Google Colab
- Kaggle
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