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 keras_metadata.pb from MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands: direct link, hf CLI and curl.
- Browser
- Download file 836 kB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands/resolve/refs%2Fpr%2F2/keras_metadata.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands@refs/pr/2/keras_metadata.pb
-
curl -L -o keras_metadata.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands/resolve/refs%2Fpr%2F2/keras_metadata.pb
836 kB
- Xet hash:
- 6df34132aaaa897ad5622743c80fabd0c41772f49e87fa82237f71d93d9c2b3f
- Size of remote file:
- 836 kB
- SHA256:
- 13e964de354d9d13fb1fb56fd8d89c6b29de0aeb397939d5b428f7a8c6a4c1ca
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