Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_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_VariationalAutoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06a9deb63e75d5aacd4ec23d5f442c0ac024df12fa27d06ae0a45bedc00a8c00
- Size of remote file:
- 524 MB
- SHA256:
- 17a86d4a6d7ce0e39732796e6145087d9196f046c4ccb7925787e9202b60bc00
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