Generator Model Training Orchestration#27
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July 1, 2026 11:35
Experiment ID:2 Run ID:5e9a052521da4320871db9625e09cf78 Run Name:agreeable-turtle-117 - ConvNeXtUNet Generator - Single 2D crop input and single 2D crop target - Cache-backed filtered nucleus crops generated from the configured data directory - L1 optimization objective with L2, PSNR, SSIM, and Pearson correlation metric logging - Normalization of input and target images using the max possible pixel intensity - Training on entire initial dataset - Sigmoid output activation function when computing metrics and loss and saving images
wli51
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Jul 13, 2026
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@MattsonCam great work! This is a very difficult dataset to train on. Hopefully improvements but I wouldn't be surprised if the dynamic range still breaks everything.
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Thanks for the review @wli51 ! There will be a couple more prs shortly. Merging now |
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This PR expands the generator-model training workflow into a more reusable orchestration pattern and simplifies the training stack around the current reconstruction-based approach. It adds broader run configuration for optimization, checkpointing, resume behavior, and training/evaluation precision control, while also improving deterministic data handling and evaluation behavior. The result is a cleaner foundation for training current and future generator models with more consistent experiment management.