Instructions to use logasja/auramask-vggface-inkwell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use logasja/auramask-vggface-inkwell 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-vggface-inkwell") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b71893e27ed8304a5e3c6672222b052bfa28437f628b1b6ac0194118c886345c
- Size of remote file:
- 548 MB
- SHA256:
- bd662ff49bba53249dab58bd73d7bd22bd41fc6f7495791f4d5c05cb655a11e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.