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
| { | |
| "model_name": "Beyond Backscatter GRD/GEE coherence estimator", | |
| "framework": "tensorflow_keras", | |
| "weights_filename": "model.weights.h5", | |
| "weights_format": "legacy_keras_h5", | |
| "config_filename": "config.yaml", | |
| "source_weight_set": "GEE", | |
| "input_convention": "two Sentinel-1 GRD linear sigma0 images from Google Earth Engine, channel order [t1, t2]", | |
| "model_preprocessing": "linear sigma0 -> dB -> clip [-20, 0] -> normalize [0, 1]", | |
| "output_convention": "predicted coherence in [0, 1], first model output channel", | |
| "github_repo": "https://github.com/FPSica/BeyondBackscatter", | |
| "colab_notebook": "https://colab.research.google.com/github/FPSica/BeyondBackscatter/blob/main/notebooks/back2coh_grd_gee_colab.ipynb" | |
| } | |