Update imaging ml benchmarks
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Divide the current ML benchmark into three major steps: 1. E/p cut study, 2. ML data preprocessing (including E/p cut), 3. ML training and benchmarking.
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Improve how the python scripts handle the data to avoid insane usage of memories with big data.
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Implement a 2d scan of E/p cut so it automatically chooses the best cut on [layer, E/p].
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Add a script for ML data preprocessing so the benchmark can directly take standard reconstruction output (no need for MLSorter/MLCombiner which are not used in other reconstruction option files).
Edited by Chao Peng
Merge request reports
Activity
assigned to @cpeng
added 1 commit
- c274ccdf - make batch size configurable and more minor improvments
added 1 commit
- 29968907 - add random seed to ml_training (for reproducibility)
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