Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_12Bands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_12Bands with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_12Bands") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db6615b19435289114a0590ddc29a01f1f4fd5921dd91294d79b6aea981227f6
- Size of remote file:
- 515 MB
- SHA256:
- 89c91dd5c63cff525918fabc13798a2dab1d8961a7a346d7c52ab41063cd9c34
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