Instructions to use logasja/auramask-ensemble-dogpatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use logasja/auramask-ensemble-dogpatch with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://logasja/auramask-ensemble-dogpatch") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 6313cc49144d632f46c988fc3f307b7dfd30a92433a7d60021357251bee6b53f
- Size of remote file:
- 103 kB
- SHA256:
- 505be1345a8674228852f0fab498b1d4177429197cabb566d0fa5e68772b4f0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.