Instructions to use Ajibola/PaViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Ajibola/PaViT with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Ajibola/PaViT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de3eeeb7a62dd687cec97d11273a6d9702d3b6675f019a05785a8b32f4b448bf
- Size of remote file:
- 889 kB
- SHA256:
- 0fd91bbbe5eb21862200764802880b396bc1459912f029580b9b1d5028b4e3c4
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