Instructions to use niobures/YAMNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use niobures/YAMNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://niobures/YAMNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2949d585c5df9124fb840e10a1d22b8fe44a822b68f57e9a0af6de72eb01099d
- Size of remote file:
- 3.15 MB
- SHA256:
- df952d80603fdd348895c1d49816ab57e2a2e8c0fd37c3859fb31947bf8afa16
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