OpenHUTB/nn
Apr 2026 – Apr 2026
1 Month active
Languages Used
Python
Technical Skills
GitPython scriptingautonomous systemscomputer visiondata visualizationdeep learning
oo333321
PROFILE
Oo333321
Worked on the OpenHUTB/nn repository to establish the foundational architecture for an autonomous vehicle perception and planning system. Developed project scaffolding and implemented YOLOv3-based object detection alongside CARLA-driven trajectory planning, focusing on modularity and maintainability. Enhanced the visualization module by introducing category-specific color coding to improve data interpretation and updated documentation to guide model weight management. Prioritized repository hygiene by removing unnecessary log files and refining the .gitignore configuration. Validated the local development environment to ensure readiness for end-to-end integration. Utilized Python, Git, and deep learning frameworks, demonstrating a methodical approach to building robust autonomous systems infrastructure.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,327
Activity Months1
Your Network
263 peopleWork History
April 2026
2 Commits • 2 Features
Apr 1, 2026OpenHUTB/nn – 2026-04 Monthly Summary: Delivered foundational autonomous vehicle capabilities and visualization improvements, with strong emphasis on code hygiene and local validation. Key deliverables include: project scaffolding and groundwork for YOLOv3-based perception plus CARLA-based trajectory planning (commit 371c668...); category-specific color coding in the visualization module with updated model-weights guidance (commit 7aabcd4...); repo hygiene fixes removing logged artifacts and expanding .gitignore to prevent recurrence; successful local run preparations laying groundwork for end-to-end integration. Technologies demonstrated: YOLOv3, CARLA, Python, Git, visualization design, and documentation practices.
2 Commits • 2 Features
Apr 1, 2026OpenHUTB/nn – 2026-04 Monthly Summary: Delivered foundational autonomous vehicle capabilities and visualization improvements, with strong emphasis on code hygiene and local validation. Key deliverables include: project scaffolding and groundwork for YOLOv3-based perception plus CARLA-based trajectory planning (commit 371c668...); category-specific color coding in the visualization module with updated model-weights guidance (commit 7aabcd4...); repo hygiene fixes removing logged artifacts and expanding .gitignore to prevent recurrence; successful local run preparations laying groundwork for end-to-end integration. Technologies demonstrated: YOLOv3, CARLA, Python, Git, visualization design, and documentation practices.
April 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
GitPython scriptingautonomous systemscomputer visiondata visualizationdeep learningfull stack development
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