Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands: direct link, hf CLI and curl.
- Browser
- Download file 5.94 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands/resolve/refs%2Fpr%2F1/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands@refs/pr/1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands/resolve/refs%2Fpr%2F1/saved_model.pb
5.94 MB
- Xet hash:
- 4b1aa293dfe9e98ec5c64d9b828bb63bfeb51a93b25a8931919a50e3ee4a889e
- Size of remote file:
- 5.94 MB
- SHA256:
- cbca52b500d77981c19ba4f58d3a44a7fe13b52a3fcc16c677789d526b28d758
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