pskcci/DX-01
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookMarkdownPythonC++
Technical Skills
Basic Python ProgrammingConvolutional Neural NetworksData VisualizationDeep LearningDocumentationGradient Descent
kimhunwoo
PROFILE
Kimhunwoo
Over two months, Kwangho Woo developed and maintained the pskcci/DX-01 repository, focusing on machine learning and computer vision solutions. He consolidated ML/AI homework content, including OpenCV-based image processing and convolutional neural networks for Fashion MNIST, while establishing a modular project structure with clear documentation. In December, he engineered a real-time pose estimation and object detection system using OpenVINO and Python, featuring live video processing and reusable setup scripts. His work emphasized reproducibility, maintainability, and streamlined onboarding, with careful repository hygiene and asset management. The depth of implementation demonstrated solid proficiency in Python, OpenCV, and modern deep learning workflows.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
13Total
Bugs
1
Commits
13
Features
4
Lines of code
5,099
Activity Months2
Your Network
1012 people
Work History
December 2024
6 Commits • 2 Features
Dec 1, 2024December 2024 summary for pskcci/DX-01: Key features delivered include a real-time OpenVINO-based Pose Estimation and Object Detection system with live video display, pose decoding, drawing, and environment setup for both models, plus a reusable setup script and demo assets. Additionally, presentation materials and documentation were updated to transition from PPTX to ODP formats. Minor repository hygiene improvements, including asset cleanup and import fixes, were completed to support reliable demos. Impact: established a solid, reusable demo pipeline for real-time analytics, enabling faster stakeholder demonstrations and future deployment. Skills demonstrated: OpenVINO, real-time video processing, pose estimation, object detection, Python scripting, environment setup, assets management, and documentation discipline.
6 Commits • 2 Features
Dec 1, 2024December 2024 summary for pskcci/DX-01: Key features delivered include a real-time OpenVINO-based Pose Estimation and Object Detection system with live video display, pose decoding, drawing, and environment setup for both models, plus a reusable setup script and demo assets. Additionally, presentation materials and documentation were updated to transition from PPTX to ODP formats. Minor repository hygiene improvements, including asset cleanup and import fixes, were completed to support reliable demos. Impact: established a solid, reusable demo pipeline for real-time analytics, enabling faster stakeholder demonstrations and future deployment. Skills demonstrated: OpenVINO, real-time video processing, pose estimation, object detection, Python scripting, environment setup, assets management, and documentation discipline.
December 2024
November 2024
7 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for pskcci/DX-01: Delivered a cohesive ML/AI homework suite and foundational project setup, aligning curriculum content with repository structure for scalable participation and maintainability. Key features include consolidation of ML/AI homework content (OpenCV image processing, CNN on Fashion MNIST, gradient-descent visualizations, and basic Python/NumPy exercises) and initial project scaffolding with README updates reflecting participant information. Major cleanup removed deprecated or unfinished notebooks to reduce noise and technical debt. Overall impact: faster curriculum deployment, clearer onboarding, and a cleaner, reproducible codebase. Demonstrated proficiency in Python, OpenCV, neural networks, NumPy, data visualization, and Git-based collaboration.
November 2024
7 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for pskcci/DX-01: Delivered a cohesive ML/AI homework suite and foundational project setup, aligning curriculum content with repository structure for scalable participation and maintainability. Key features include consolidation of ML/AI homework content (OpenCV image processing, CNN on Fashion MNIST, gradient-descent visualizations, and basic Python/NumPy exercises) and initial project scaffolding with README updates reflecting participant information. Major cleanup removed deprecated or unfinished notebooks to reduce noise and technical debt. Overall impact: faster curriculum deployment, clearer onboarding, and a cleaner, reproducible codebase. Demonstrated proficiency in Python, OpenCV, neural networks, NumPy, data visualization, and Git-based collaboration.
Activity
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Quality Metrics
Correctness79.4%
Maintainability78.4%
Architecture79.4%
Performance78.4%
AI Usage27.6%
Skills & Technologies
Programming Languages
C++Jupyter NotebookMarkdownPython
Technical Skills
API IntegrationBasic Python ProgrammingComputer VisionConvolutional Neural NetworksData VisualizationDeep LearningDocumentationGradient DescentImage ProcessingKerasMachine LearningMachine Learning FundamentalsMatplotlibNeural NetworksNumPy
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