GaochangWu/FMF-Benchmark
This is a cross-modal benchmark for industrial anomaly detection.
27
Stars
3
Forks
21.3
Radar Score
Stable
Momentum
Scoring Breakdown
Impact9.4
Velocity24.4
Health35.9
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Python
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🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
C
Based on maintenance, issue response, and release cadence.
Radar Score
21.3
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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