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talhaMah56talhaMah56/claude_skills/image-preprocessing-and-tiling/SKILL.md

image-preprocessing-and-tiling

Skill0 starsChanged 2 months agoNo licenceView on GitHub

Build and debug image preprocessing pipelines for deep learning when images are large, multi-channel, or scientifically calibrated - satellite/aerial GeoTIFF, Sentinel/Landsat multispectral stacks, whole-slide pathology (SVS/NDPI/OpenSlide), CT/MRI volumes, or any raster too big for the GPU. Use when the task mentions tiling, patching, sliding window, chip extraction, stride/overlap, seams when stitching predictions, reassembling tiles into a full-scene mask, normalization statistics, per-channel mean/std, percentile clipping, nodata poisoning stats, albumentations, image+mask augmentation, additional_targets, class imbalance in segmentation, empty/background tiles, slow DataLoader num_workers on Windows, worker seeding and reproducible augmentation, or converting a dataset to memmap numpy / WebDataset / LMDB / zarr. Also use when a segmentation or dense-prediction model trains fine but produces grid-shaped seams, all-background outputs, or scores far worse at inference than in validation.

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14 rules. About 0 are common, 10 are distinctive, and 4 are very specific to this project.

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