Instructions to use jubayer009/retinal_efficientnetv2b3.keras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use jubayer009/retinal_efficientnetv2b3.keras with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://jubayer009/retinal_efficientnetv2b3.keras") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 435d060cb0501131e425962886a43290e959472fec7dde668d4b794f068d7f61
- Size of remote file:
- 85.1 MB
- SHA256:
- bacac792fe096ecdba0fdabc7ce412449ea01d35bcba213e0b01ba5a43c95153
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