Paired dataset audit
Skill StaryMoon/paired-dataset-audit-skill/skills/paired-dataset-audit
Audit paired image datasets for missing counterparts, corrupt files, shape or channel mismatches, duplicate content, and suspicious samples, then produce a JSON report and visual contact sheet. Use before training restoration, translation, segmentation, super-resolution, or other paired vision models.From its SKILL.md
npx -y skills add StaryMoon/paired-dataset-audit-skill --skill paired-dataset-auditAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.7 KB, 298 tokens by cl100k_base, as published. Nobody here has run it
Paired Dataset Audit
Check the dataset before spending GPU time. Keep the audit read-only: do not rename, delete, or resize source files unless the user separately approves a repair plan.
Workflow
- Identify the two dataset roots and confirm how filenames correspond.
- Run the default relative-stem matcher:
python3 scripts/audit_paired_dataset.py data/input data/target \
--output output/dataset-audit
- Inspect
report.mdandpair-samples.png. - Treat missing pairs, corrupt files, and shape mismatches as blocking by default.
- Review duplicate groups before calling them leakage; legitimate burst frames or repeated masks can share content.
- If repair is requested, create a separate repaired copy and retain
audit.jsonas provenance.
Matching rules
The default key is the relative path without the final extension. scene01/rain/0001.png matches scene01/rain/0001.jpg. Use --flat-stem only when both roots have globally unique stems.
Quality rules
- Never modify source directories during audit.
- Verify decoded dimensions and modes, not only filenames.
- Report duplicate hashes separately for each side.
- Sample contact sheets deterministically so CI results remain stable.
- Exit non-zero with
--strictwhen blocking defects exist.
See references/repair-policy.md before applying fixes.
What ships with it: 3 files
8.1 KB alongside SKILL.md, 1 of them executable
agents/
- openai.yaml239 B
references/
- repair-policy.md631 B
scripts/
- audit_paired_dataset.pyruns7.3 KB