Instructions to use yasinelh/retinal_vessel_U-Net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use yasinelh/retinal_vessel_U-Net with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://yasinelh/retinal_vessel_U-Net") - Notebooks
- Google Colab
- Kaggle
I present a demo showcasing retinal vessel segmentation using the U-Net model, which is a well-known and widely used model in medical image segmentation. The model was trained on the DRIVE dataset, and the training process was conducted on Google Colab. The demo itself was built using Streamlit and deployed using localtunnel.
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