KU-BIG/KUBIG_2025_FALL
Jul 2025 – Jan 2026
3 Months active
Languages Used
Jupyter NotebookPythonMarkdownNoneUnknown
Technical Skills
CNNComputer VisionData PreprocessingDataLoaderDeep LearningImage Classification
yeopgee
PROFILE
Yeopgee
Over three months, Jiyoung Lee developed and documented deep learning workflows in the KU-BIG/KUBIG_2025_FALL repository, focusing on computer vision and generative modeling. She built end-to-end Jupyter notebooks for image classification using CNN, ResNet, and Vision Transformer architectures on MNIST and CIFAR datasets, implementing data preprocessing, model training, and evaluation in Python and PyTorch. Lee also designed a reproducible research platform for dog-aging experiments, introducing an AGE-CGAN framework with self-attention and PatchGAN discriminators, and established robust project scaffolding with comprehensive READMEs. Her work emphasized reproducibility, onboarding, and extensibility, demonstrating depth in model development and research documentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
7
Lines of code
19,619
Activity Months3
Your Network
27 people
Work History
January 2026
5 Commits • 3 Features
Jan 1, 2026January 2026 (KU-BIG/KUBIG_2025_FALL) monthly summary: Key features delivered include initializing the Beyond CNN 1 project scope and cleanup of unnecessary KUBIG conference components, along with comprehensive project documentation and a CIFAR-100 image classification notebook using ResNet with data augmentation. No major bugs fixed this month. Overall impact: clarified project scope for Beyond CNN 1, improved onboarding and knowledge transfer through README updates, and established a ready-to-run CIFAR-100 experiment notebook to accelerate model development. Technologies demonstrated: project scoping and cleanup, documentation best practices, Git versioning, Jupyter notebooks, PyTorch/ResNet, data augmentation techniques.
5 Commits • 3 Features
Jan 1, 2026January 2026 (KU-BIG/KUBIG_2025_FALL) monthly summary: Key features delivered include initializing the Beyond CNN 1 project scope and cleanup of unnecessary KUBIG conference components, along with comprehensive project documentation and a CIFAR-100 image classification notebook using ResNet with data augmentation. No major bugs fixed this month. Overall impact: clarified project scope for Beyond CNN 1, improved onboarding and knowledge transfer through README updates, and established a ready-to-run CIFAR-100 experiment notebook to accelerate model development. Technologies demonstrated: project scoping and cleanup, documentation best practices, Git versioning, Jupyter notebooks, PyTorch/ResNet, data augmentation techniques.
January 2026
August 2025
4 Commits • 2 Features
Aug 1, 2025August 2025 summary: Established a reproducible research platform and delivered first features for dog-aging GAN experiments in KU-BIG/KUBIG_2025_FALL. Key deliverables: AGE-CGAN training framework with self-attention, multiscale PatchGAN discriminators, EMA optimization, and a dog aging dataset with Young/Senior preprocessing. Also produced project scaffolding and a comprehensive README covering objectives, datasets, architectures, experiments, results, usage, and next steps. Impact: faster, more reproducible experimentation, improved onboarding, and stronger governance for research artifacts. Technologies: PyTorch-based GANs, self-attention, EMA, replay buffers, data preprocessing, and thorough documentation. No explicit bug fixes documented this month.
August 2025
4 Commits • 2 Features
Aug 1, 2025August 2025 summary: Established a reproducible research platform and delivered first features for dog-aging GAN experiments in KU-BIG/KUBIG_2025_FALL. Key deliverables: AGE-CGAN training framework with self-attention, multiscale PatchGAN discriminators, EMA optimization, and a dog aging dataset with Young/Senior preprocessing. Also produced project scaffolding and a comprehensive README covering objectives, datasets, architectures, experiments, results, usage, and next steps. Impact: faster, more reproducible experimentation, improved onboarding, and stronger governance for research artifacts. Technologies: PyTorch-based GANs, self-attention, EMA, replay buffers, data preprocessing, and thorough documentation. No explicit bug fixes documented this month.
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 — KU-BIG/KUBIG_2025_FALL: Delivered two end-to-end image-classification notebooks to accelerate prototyping and evaluation of CNN/ResNet-like architectures for MNIST and Vision Transformer (ViT) implementations for CIFAR-10. Each artifact encompasses data preprocessing, model definition, training, evaluation, and prediction, with clear repository traceability. These deliverables enable rapid benchmarking, stakeholder demos, and a solid foundation for future production-ready pipelines.
2 Commits • 2 Features
Jul 1, 2025July 2025 — KU-BIG/KUBIG_2025_FALL: Delivered two end-to-end image-classification notebooks to accelerate prototyping and evaluation of CNN/ResNet-like architectures for MNIST and Vision Transformer (ViT) implementations for CIFAR-10. Each artifact encompasses data preprocessing, model definition, training, evaluation, and prediction, with clear repository traceability. These deliverables enable rapid benchmarking, stakeholder demos, and a solid foundation for future production-ready pipelines.
July 2025
Activity
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Quality Metrics
Correctness86.4%
Maintainability83.6%
Architecture86.4%
Performance83.6%
AI Usage23.6%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownNonePythonUnknown
Technical Skills
Basic File CreationCNNComputer VisionData PreprocessingData VisualizationDataLoaderDeep LearningGenerative Adversarial Networks (GANs)Image ClassificationImage GenerationImage ProcessingJupyter NotebookMachine LearningMatplotlibModel Evaluation
Repositories Contributed To
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