voxel51/fiftyone-plugins
Jan 2025 – Jan 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
API DevelopmentCode CleanupCode RefactoringData AnalysisMachine Learning MetricsMetric Calculation
Manushree Gangwar
PROFILE
Manushree Gangwar
Developed and delivered new Absolute Error and Mean Absolute Error metrics for the voxel51/fiftyone-plugins repository, enabling per-sample and per-frame evaluation within data-centric machine learning workflows. The work involved extensive refactoring of metric classes in Python to improve robustness and maintainability, as well as aligning regression tagging and introducing lower_is_better semantics for consistent metric behavior. API enhancements included moving get_fields and adding compute_by_sample to support granular analytics. Commit consolidation ensured a cleaner contribution history and improved traceability. These changes enhanced the plugin’s ability to provide precise metric calculations and streamlined integration with existing machine learning pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
1
Lines of code
321
Activity Months1
Your Network
134 peopleWork History
January 2025
8 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01) — Performance and Metrics Enhancements for FiftyOne Plugins. Key feature delivered: Absolute Error and Mean Absolute Error metrics with per-sample and per-frame evaluation, accompanied by substantial refactors to metric classes for robustness. This work also includes API and behavior improvements such as regression tagging updates and lower_is_better semantics. Additional API surface improvements include moving get_fields and introducing compute_by_sample to support granular analytics. Commit consolidation across the feature work improves maintainability and traceability. Overall, these changes enable more precise evaluation, better pipeline integration, and faster debugging in data-centric ML workflows.
8 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01) — Performance and Metrics Enhancements for FiftyOne Plugins. Key feature delivered: Absolute Error and Mean Absolute Error metrics with per-sample and per-frame evaluation, accompanied by substantial refactors to metric classes for robustness. This work also includes API and behavior improvements such as regression tagging updates and lower_is_better semantics. Additional API surface improvements include moving get_fields and introducing compute_by_sample to support granular analytics. Commit consolidation across the feature work improves maintainability and traceability. Overall, these changes enable more precise evaluation, better pipeline integration, and faster debugging in data-centric ML workflows.
January 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability83.8%
Architecture82.6%
Performance82.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
API DevelopmentCode CleanupCode RefactoringData AnalysisMachine Learning MetricsMetric CalculationObject-Oriented ProgrammingPlugin DevelopmentPython DevelopmentRefactoringSoftware DevelopmentSoftware Engineering
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