Instructions to use MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_ViT_Autoencoder_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_ViT_Autoencoder_12Bands") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands: direct link, hf CLI and curl.
- Browser
- Download file 15.9 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands/resolve/main/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands/resolve/main/saved_model.pb
15.9 MB
- Xet hash:
- 88e338d4234dc3e29e33254a1bab0132ed3bd88b482ef1d1e789ec0e8242f830
- Size of remote file:
- 15.9 MB
- SHA256:
- ae95e79dcdcc810747c60953f0017870e257c26c520b0965fc9fc83f05b75060
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