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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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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## Model description
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Autoencoder model trained to compress information from sentinel-2 satellite images using Resnet50 V2 as decoder backbone to extract features.
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The latent space of the model is given by 1024 neurons which can be used to generate embeddings from the sentinel-2 satellite images.
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The model was trained using bands 2, 3 and 4 of the Sentinel-2 satellites and for the full Colombia dataset.
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The input shape of the model is 224, 224, 3. To extract features you should remove the last layer.
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## Intended uses & limitations
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The model was trained with images of 81 different cities in Colombia, however it may require fine tuning or retraining to learn from other contexts such as countries and other continents.
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## Training and evaluation data
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The model was trained with satellite images of 81 different cities in Colombia extracted from sentinel-2.
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## Training procedure
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