Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB 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_Autoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
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README.md
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The model was trained with satellite images of 10 different cities in Colombia extracted from sentinel-2 using RGB bands using an asymmetric autoencoder. Images with information that could result in noise such as black images were filtered prior to training to avoid noise in the data.
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The dataset was split into train and test using 80% for train and 20% to test.
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## Training procedure
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### Training hyperparameters
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The model was trained with satellite images of 10 different cities in Colombia extracted from sentinel-2 using RGB bands using an asymmetric autoencoder. Images with information that could result in noise such as black images were filtered prior to training to avoid noise in the data.
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The dataset was split into train and test using 80% for train and 20% to test.
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## Training procedure
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### Training hyperparameters
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