Instructions to use Aldahmashi/DR-EfficientNetB0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Aldahmashi/DR-EfficientNetB0 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Aldahmashi/DR-EfficientNetB0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0ac33e7969ab5f99e94fa67c03c44ca1eb941fb96b9e01f66a147a940fd3e6e
- Size of remote file:
- 33.4 MB
- SHA256:
- e7aa6b69911a2a913a03a6a5669bb7aaeb6bf8f8c81a2a3d92aa2420e5d297d8
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