Instructions to use seige-ml/DeepSeeNet_ADV_AMD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use seige-ml/DeepSeeNet_ADV_AMD with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://seige-ml/DeepSeeNet_ADV_AMD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c043a333fd9ac7e3d5a59f5e8168a6ab46cd2ff70aac1257734c79757edcb807
- Size of remote file:
- 4.41 MB
- SHA256:
- e3dbd7611b96940227a178fbdac124ef2870b9122cb0cb235d60de02c42cb01e
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