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:
# !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_12Bands") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands: direct link, hf CLI and curl.
- Browser
- Download file 556 Bytes
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https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_12Bands/resolve/refs%2Fpr%2F1/README.md
556 Bytes
metadata
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 | 0.0010000000474974513 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |