Instructions to use MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset 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_RGB_full_Colombia_Dataset") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 16.2 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset/resolve/main/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset/resolve/main/saved_model.pb
16.2 MB
- Xet hash:
- fe679a062f05de0057e73da0ba26930d83f97889c21ae2d764c756c2a6c30504
- Size of remote file:
- 16.2 MB
- SHA256:
- 5701db00994f47e59cec5be0168a2fe9e2c5afb4c07a780c607d1cc34dd2990e
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