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:
# 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_Resnet50V2_VariationalAutoencoder_12Bands") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b540f65e2e2334e8a31b5047bebaabb399447bca830db8a727789e4317749fe1
- Size of remote file:
- 524 MB
- SHA256:
- 2a7a6217f55e982a5436f425e2268352e1d781caa627e7279ceef92c554c9aef
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