Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_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_Resnet50V2_VariationalAutoencoder_RGB_full_Colombia_Dataset 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_RGB_full_Colombia_Dataset") - Notebooks
- Google Colab
- Kaggle
Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB_full_Colombia_Dataset / variables /variables.data-00000-of-00001
- Xet hash:
- 9aa881c6da9ffe874d0e2096d87f0e01e6cb794a3b1beeff7e8da473496a1525
- Size of remote file:
- 524 MB
- SHA256:
- 7c1844c370a734d14d0494c86c9627db20999be11f0c68fed7e50904eaacebbf
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