Lightning-AI/torchmetrics
Aug 2025 – Aug 2025
1 Month active
Languages Used
JinjaMarkdownPythonShell
Technical Skills
Backend DevelopmentCode RefactoringData ScienceFull Stack DevelopmentImage ProcessingMachine Learning
Kulbhushan Singh
PROFILE
Kulbhushan Singh
Kulbhushan Singh enhanced the Lightning-AI/torchmetrics repository by implementing per-sample VIF scoring, introducing a reduction='none' option that enables batch-level analysis of image quality metrics. He updated both functional and class-based Python implementations, ensuring comprehensive test coverage for the new feature. Additionally, he addressed edge cases in precision-recall and PR-curve calculations by refining NaN handling when thresholds are undefined, improving the reliability of binary metric evaluation. His work involved backend development, code refactoring, and metrics calculation, utilizing Python and Shell scripting. These contributions deepened the robustness of metric computation and testing, reducing the risk of misleading results in model evaluation.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
286
Activity Months1
Your Network
22 people
Work History
August 2025
3 Commits • 1 Features
Aug 1, 20252025-08 monthly summary for Lightning-AI/torchmetrics: Implemented per-sample VIF scoring via reduction='none' with end-to-end changes across functional and class-based implementations and tests; fixed NaN handling for undefined results in precision-recall thresholds and PR-curve calculations (no-threshold and TP+FP == 0 scenarios) with corresponding commits. This work improves batch-level analytics, reliability of binary metrics, and PR-curve interpretation, reducing risk of misleading results in model evaluation.
3 Commits • 1 Features
Aug 1, 20252025-08 monthly summary for Lightning-AI/torchmetrics: Implemented per-sample VIF scoring via reduction='none' with end-to-end changes across functional and class-based implementations and tests; fixed NaN handling for undefined results in precision-recall thresholds and PR-curve calculations (no-threshold and TP+FP == 0 scenarios) with corresponding commits. This work improves batch-level analytics, reliability of binary metrics, and PR-curve interpretation, reducing risk of misleading results in model evaluation.
August 2025
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaMarkdownPythonShell
Technical Skills
Backend DevelopmentCode RefactoringData ScienceFull Stack DevelopmentImage ProcessingMachine LearningMetrics CalculationSoftware EngineeringTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline