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:
- eb43c945f803d908624ee5ed31cec03e4d9112cd8ac60f6843de8296474739d0
- Size of remote file:
- 5.44 MB
- SHA256:
- 0f9afdc7b34ff08b92279a894bacce81be77cbaf638674fa2bd8dd072ff6320c
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