Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB 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_RGB") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b01e0477020788c567473aa8014e2e2af2303a0c3b89d3a0e857f308691ef440
- Size of remote file:
- 515 MB
- SHA256:
- 7edca77b620115c21034e8bd33e1dcccdaf5de3372864f9afb4912eba26f754e
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