Instructions to use FPSica/beyond-backscatter-grd-gee with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use FPSica/beyond-backscatter-grd-gee with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://FPSica/beyond-backscatter-grd-gee") - Notebooks
- Google Colab
- Kaggle
Upload model_metadata.json with huggingface_hub
Browse files- model_metadata.json +1 -0
model_metadata.json
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"model_name": "Beyond Backscatter GRD/GEE coherence estimator",
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"framework": "tensorflow_keras",
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"weights_filename": "model.weights.h5",
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"config_filename": "config.yaml",
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"source_weight_set": "GEE",
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"input_convention": "two Sentinel-1 GRD linear sigma0 images from Google Earth Engine, channel order [t1, t2]",
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"model_name": "Beyond Backscatter GRD/GEE coherence estimator",
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"framework": "tensorflow_keras",
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"weights_filename": "model.weights.h5",
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"weights_format": "legacy_keras_h5",
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"config_filename": "config.yaml",
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"source_weight_set": "GEE",
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"input_convention": "two Sentinel-1 GRD linear sigma0 images from Google Earth Engine, channel order [t1, t2]",
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