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 a two-month period, contributed to the pskcci/DX-01 repository by developing and refining machine learning and computer vision solutions. Built a real-time OpenVINO-based pose estimation and object detection system with live video processing, integrating Python scripting and OpenCV for environment setup and demo asset management. Consolidated ML/AI homework content, including convolutional neural networks on Fashion MNIST and gradient descent visualizations, while restructuring the repository for clarity and maintainability. Enhanced onboarding through improved documentation and project scaffolding, and maintained code quality by removing deprecated notebooks. Demonstrated proficiency in Python, OpenVINO, NumPy, and collaborative Git-based workflows throughout the project.
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
1142 peopleShared Repositories
11
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
Repositories Contributed To
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