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:
- 36452a1cf99207cd8d780d52bd0583a7de717a2364708d55dcd14d5a83cb8b09
- Size of remote file:
- 828 kB
- SHA256:
- 5b3cf96cb335faca39643324c88f72d9182d9e4ed033f724189954c59f8c4352
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