DatasetOps Vision Lab audits local image-classification datasets before training or test evaluation. It combines a Python computer vision engine with a local Next.js dashboard for transparent readiness scoring and evidence-based recommendations.
The primary user is a data science learner or builder preparing image datasets for modeling who needs to catch obvious readiness risks before trusting training or validation results.
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Run the Python engine:
pnpm engine:scan -- --path ./dataset --out ./reports/latest-report.json
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Import
latest-report.jsoninto the dashboard. -
Review quality score, class distribution, leakage, duplicates, image quality, corrupt files, and recommendations.
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Optionally use Browser Fast Scan for quick local folder structure checks.
engine/python: canonical audit engine and report exporters.apps/web: report viewer and browser Fast Scan dashboard.reports: local output folder, ignored by git except.gitkeep.
riskScore = weighted sum, qualityScore = 100 - riskScore.
Weights:
- Leakage: 30
- Imbalance: 20
- Low resolution: 15
- Duplicates: 10
- Blur: 10
- Brightness/contrast: 10
- Confusion concentration: 5
- Training models
- Classifying image content
- Semantic object understanding
- Near-duplicate/perceptual similarity detection
- Uploading datasets
- Cloud storage or accounts
- Guaranteeing model or test performance improvement