Comments (2)
Need help on how to integrate metadata into the track ome_zarr, how this will be used to generate the patch ome_zarr and then eventually the dataloader for normalization.
@alishbaimran our initial approach can be
- compute FOV statistics from
track_labels.zarr/row/col/fovN/
array. - write them to each cropped FOV in
patches.zarr
in/row/col/fovNcellM/.zattr
file. - use the statistics while loading patches for normalizing the data.
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Now obsolete since we removed patchification.
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Related Issues (20)
- Compatibility issues HOT 10
- Contrastive Learning Implementation HOT 1
- organize application scripts on GitHub rather than disk
- Illustrative test datasets HOT 4
- Predict module not giving expected output HOT 3
- API for 2D UNeXt2 model construction needs clarity HOT 2
- WIP: refactor contrastive learning code with virtual staining code HOT 6
- Overflow error seen in training and prediction steps
- switching to tensorboard logger for organelle phenotyping
- document published models on Wiki HOT 1
- Unify stem implementation HOT 1
- Time sampling for positive pair
- Support loading data from a specific resolution of a .zarr pyramid HOT 1
- CLI for multiple training tasks HOT 4
- Move to NumPy style docstrings? HOT 1
- Model size mismatch error during prediction HOT 3
- Implicit negative sampling
- Hard-coded stem stride in `ContrastiveEncoder` HOT 1
- Update the projection head (normalization and size).
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