Instructions to use funlab/clipnet-fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use funlab/clipnet-fold_3 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_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 769b5cabadd1f0b62e6537d2e4dcff92e9368afdd7afd25b5a11f3eba98f1ba1
- Size of remote file:
- 1.37 MB
- SHA256:
- ce4619fcd3e672b1a9df008257aaca345ea51f09c4b77acfff4e2b1ee8e5c227
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.