Instructions to use logasja/auramask-ensemble-gingham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use logasja/auramask-ensemble-gingham with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://logasja/auramask-ensemble-gingham") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c9b2af012ba69ced81fd8081fec8a6879a989274ba328a18a9bcb8180bc298f
- Size of remote file:
- 274 MB
- SHA256:
- 0bd71343f65be445bffb77353063d3122864fc91d28248073b0c8614bc04d97e
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