Instructions to use atuml/FishNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use atuml/FishNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://atuml/FishNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- efcbfbbbf9cf101d8a863349271b3d71dfe1a2f3dcb34acc26b5cb63b1a5f9cb
- Size of remote file:
- 44.1 MB
- SHA256:
- 987fa64e45588ae916ea1468dc5487366bb10690e925f2dac21c0d7e691eb9de
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