Instructions to use funlab/clipnet-fold_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use funlab/clipnet-fold_4 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://funlab/clipnet-fold_4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b062f3295992f85e4a5ac37b3c6e146f2738e5919524833dd22f239af60d5f20
- Size of remote file:
- 1.37 MB
- SHA256:
- e12972a1711618416b3664fc417b1a5da1ac9458b38c475125f78a6a5681801d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.