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
Worked on the Lightning-AI/torchmetrics repository to enhance metric reliability and batch-level analysis for machine learning workflows. Developed a per-sample scoring option for the VIF metric by introducing a reduction='none' mode, updating both functional and class-based implementations and expanding test coverage. Addressed edge cases in precision-recall calculations by refining NaN handling when thresholds are undefined, ensuring accurate PR-curve interpretation. Leveraged Python and Jinja for backend development, code refactoring, and robust testing. This work improved the accuracy and interpretability of binary metrics, reducing the risk of misleading results during model evaluation and strengthening the overall software engineering foundation.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
286
Activity Months1
Your Network
27 peopleShared Repositories
27
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
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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
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