OpenHUTB/nn
Mar 2026 – Apr 2026
2 Months active
Languages Used
PythonYAML
Technical Skills
PythondocumentationmkdocsComputer VisionData AnalysisData Processing
SEMHAQ
PROFILE
Semhaq
Contributed to the OpenHUTB/nn repository by developing a YOLO-based vehicle counting system with command-line access, multi-version support, and CARLA simulator integration. Leveraged Python and deep learning to implement region-based and bidirectional counting variants, optimizing parameters for simulated datasets. Introduced an accuracy measurement framework to compare detection results against ground truth, supporting metrics such as ACC and F1. Enhanced repository hygiene by refactoring code for maintainability, updating documentation with MkDocs, and clarifying installation steps. Focused on improving onboarding and documentation reliability, the work emphasized computer vision, data processing, and model training to support robust, reproducible research and development workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
4
Lines of code
361,976
Activity Months2
Your Network
263 peopleWork History
April 2026
10 Commits • 3 Features
Apr 1, 2026April 2026 monthly summary for OpenHUTB/nn. Delivered a comprehensive YOLO-based vehicle counting stack with CLI access, multi-version support, and CARLA integration. Refactored core components for maintainability, introduced region-based and up/down counting variants, and expanded model coverage with YOLO12 on CARLA datasets. Implemented an accuracy measurement framework to quantify performance against ground truth and enhanced documentation and repository hygiene to accelerate adoption and onboarding.
10 Commits • 3 Features
Apr 1, 2026April 2026 monthly summary for OpenHUTB/nn. Delivered a comprehensive YOLO-based vehicle counting stack with CLI access, multi-version support, and CARLA integration. Refactored core components for maintainability, introduced region-based and up/down counting variants, and expanded model coverage with YOLO12 on CARLA datasets. Implemented an accuracy measurement framework to quantify performance against ground truth and enhanced documentation and repository hygiene to accelerate adoption and onboarding.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (OpenHUTB/nn): Documentation enhancements focusing on MkDocs setup and requirements to enable reliable docs generation and math support. Delivered a clarified installation flow and updated dependencies to ensure consistent builds. These changes improve developer onboarding and reduce doc-generation issues, aligning with our goal of higher-quality, maintainable documentation.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (OpenHUTB/nn): Documentation enhancements focusing on MkDocs setup and requirements to enable reliable docs generation and math support. Delivered a clarified installation flow and updated dependencies to ensure consistent builds. These changes improve developer onboarding and reduce doc-generation issues, aligning with our goal of higher-quality, maintainable documentation.
Activity
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Quality Metrics
Correctness91.0%
Maintainability83.6%
Architecture87.2%
Performance81.8%
AI Usage54.6%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Computer VisionData AnalysisData ProcessingDeep LearningMachine LearningModel TrainingPythonPython DevelopmentVideo Processingdocumentationmkdocs
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