Refactor normalization, evaluation, and preview generation for z-scored training#30
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MattsonCam merged 9 commits intoJul 15, 2026
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Jul 14, 2026
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LGTM! Great additions to the normalization and export module.
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July 14, 2026 17:57
Experiment ID:3 Run ID:763e5f09e64b455ba6981cfdf5fa60d7 Run Name:bittersweet-tern-183 Optimization of a DAPI-to-Gold image-to-image translation model with: - ConvNeXtUNet Generator - Single 2D crop input and single 2D crop target - Cache-backed filtered nucleus crops generated from the configured data directory - Train-split z-score normalization for inputs and targets - L1 optimization objective in z-score space with denormalized L2, PSNR, SSIM, and Pearson correlation metric logging - Resampling of dataset w/o anti-aliasing -> dtype clipping (to stay within the correct range) -> z-score normalization - Means and stds from the training set were stored for z-scoring of the validation set - No final activation function after the model output (such as sigmoid)
Experiment ID:3 Run ID:4feeef6283384834b907e9b1af611524 Run Name:aged-dove-229 Optimization of a DAPI-to-Gold image-to-image translation model with: - ConvNeXtUNet Generator - Single 2D crop input and single 2D crop target - Cache-backed filtered nucleus crops generated from the configured data directory - Train-split z-score normalization for inputs and targets - L1 optimization objective in z-score space with denormalized L2, PSNR, SSIM, and Pearson correlation metric logging - Resampling of dataset w/o anti-aliasing -> dtype clipping (to stay within the correct range) -> z-score normalization - Means and stds from the training set were stored for z-scoring of the validation set - No final activation function after the model output (such as sigmoid)
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Thanks for the review @wli51 ! Merging now |
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This pr switches the training pipeline to train-split z-score normalization, removes the output sigmoid so predictions stay aligned with z-scored targets during optimization, computes validation loss and reconstruction metrics in original intensity space, and improves saved example images by denormalizing and applying robust percentile-based display scaling.