This section addresses model training. We will follow the following steps to get our model trained and ready, in order to perform gapfilling:
- Download the code of the implementation from the author’s repository.
- Use the code to train the model. Model weights will be saved in checkpoint files.
- Export a trained SavedModel from an existing checkpoint.
- Use the SavedModel with the OTBTF TensorflowModelServe application to perform the optical image restoration.
Many practitioners like to have full control of their training process, and will prefer the TensorFlow Python API rather than using the TensorflowModelTrain application. Indeed, TensorflowModelTrain hides the complexity of the training ...
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