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Deep Learning for Seismic Imaging and Interpretation
License: MIT License
This project forked from microsoft/seismic-deeplearning
Deep Learning for Seismic Imaging and Interpretation
License: MIT License
Add config and supporting scripts. Validate on a sample dataset (see #2 )
The build failed when updating patch to patch_size in ablation.sh. Recreate this failed build so it can be investigated further.
Modify existing code samples and cofigs to include the option of resuming training.
For example: given a checkpoint from epoch 100 run the training for 50 more epochs.
Restart where training left off.
Update: Dutch_F3 notebook
Re-use min_epoch param in the config
param for weighs-path to resume -- double check what param we reuse
Add empty init to cv_lib/cv_lib/segmentation/dutchf3 so that our azureml_requirements.txt for AML can reference the microsoft repo and not the forked repo
Add Segy data prep utils and needed unit tests (see also #1 ) to deepseimic repo
Currently the code assumes hardcoded "test" folders and splits.
Support for custom paths will make it possible to easily test on separate volumes of seismic data.
Section slices could have quite a bit of duplicated data.
To shorten training time you could split every other (or even more) sections.
Work with Deepseimic team and pick a dataset that we can use to run AML pipeline on
This functionality enables train\test on separate input seismic volumes
Segy management unit test need to work with publicly available tiny segy file
Add data normalization utils and needed unit tests (see also #1 ) to deepseimic repo
Currently the prepare_dutchf3.py selects the edge of the cube as test and the core as train.
It is important to train with all parts of the cube so when we run the tests/validation we have more diverse data
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