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:
# !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_Resnet50V2_VariationalAutoencoder_12Bands") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands: direct link, hf CLI and curl.
- Browser
- Download file 556 Bytes
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_12Bands/resolve/refs%2Fpr%2F1/README.md
556 Bytes
| library_name: keras | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| | Hyperparameters | Value | | |
| | :-- | :-- | | |
| | name | Adam | | |
| | learning_rate | 9.999999747378752e-05 | | |
| | decay | 0.0 | | |
| | beta_1 | 0.8999999761581421 | | |
| | beta_2 | 0.9990000128746033 | | |
| | epsilon | 1e-07 | | |
| | amsgrad | False | | |
| | training_precision | float32 | | |