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
Download saved_model.pb from MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB: direct link, hf CLI and curl.
- Browser
- Download file 5.93 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB/resolve/refs%2Fpr%2F1/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB@refs/pr/1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB/resolve/refs%2Fpr%2F1/saved_model.pb
5.93 MB
- Xet hash:
- cc036d6c733775b4de4ac77523686f369103735926a7113a94e591952931c806
- Size of remote file:
- 5.93 MB
- SHA256:
- 9376601883f1459cb2bf60623d354e4c77df53d21cc3ebd969d026095bff20b5
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