Instructions to use shimaa22/FingerVeinFeatureEtractionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shimaa22/FingerVeinFeatureEtractionModel with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://shimaa22/FingerVeinFeatureEtractionModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32a69ea4e54c64bd79e565826b5beaca490db9afa0b19e93d4d8c4bfb0b7c9a2
- Size of remote file:
- 34.8 MB
- SHA256:
- 811aa09fd8640dfbd8d3a60267bd65a12d56955f55f340f998a6f7d993b75cdb
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