Instructions to use TweeeZT/Nutrivision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use TweeeZT/Nutrivision with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://TweeeZT/Nutrivision") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a961c712bc705771b7bedd917a1aa711b4ee59f1f9b52d6cb59db39fa3ccec54
- Size of remote file:
- 39.2 MB
- SHA256:
- 2bff68310866a0a1f7aca6629ff573ab188cf47a8e16eadc502c0d3ff5132bfc
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