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:
# 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_ViT_Autoencoder_12Bands") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88e338d4234dc3e29e33254a1bab0132ed3bd88b482ef1d1e789ec0e8242f830
- Size of remote file:
- 15.9 MB
- SHA256:
- ae95e79dcdcc810747c60953f0017870e257c26c520b0965fc9fc83f05b75060
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.