KU-BIG/KUBIG_2025_FALL
Jul 2025 – Aug 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
CNNComputer VisionData PreprocessingData VisualizationDeep LearningImage Classification
hwang10-22
PROFILE
Hwang10-22
During two months on the KU-BIG/KUBIG_2025_FALL repository, Hwagyun Go developed and consolidated deep learning pipelines for computer vision tasks using Python and PyTorch. He unified MNIST digit classification experiments with reusable CNN and ResNet modules, streamlining data preprocessing, model training, and evaluation in Jupyter Notebooks. Hwagyun also implemented a YOLOv1 object detection workflow on Pascal VOC, optimizing GPU usage and providing custom dataset utilities. Additionally, he built a Vision Transformer for CIFAR-10 and established a modular framework for MNIST-based VAE and GAN experiments. His work emphasized reproducibility, rapid prototyping, and maintainable code, supporting robust experimentation and onboarding.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
7,241
Activity Months2
Your Network
28 peopleShared Repositories
28
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Haeune-JeonMember
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Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly Summary - August 2025 (KU-BIG/KUBIG_2025_FALL) Overview: Implemented a foundational MNIST-based VAE and GAN training framework, establishing a reusable, modular pipeline for generative modeling experiments. The work emphasizes business value through rapid experimentation, reproducibility, and a clear path to iterative improvements in data generation and feature learning.
1 Commits • 1 Features
Aug 1, 2025Monthly Summary - August 2025 (KU-BIG/KUBIG_2025_FALL) Overview: Implemented a foundational MNIST-based VAE and GAN training framework, establishing a reusable, modular pipeline for generative modeling experiments. The work emphasizes business value through rapid experimentation, reproducibility, and a clear path to iterative improvements in data generation and feature learning.
August 2025
July 2025
5 Commits • 3 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on feature delivery and technical accomplishments in KU-BIG/KUBIG_2025_FALL. Delivered three major ML features with supporting maintenance work, improving experimentation speed, reproducibility, and cross-framework capabilities. Business value centers on rapid prototyping, robust evaluation, and GPU-accelerated workflows.
July 2025
5 Commits • 3 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on feature delivery and technical accomplishments in KU-BIG/KUBIG_2025_FALL. Delivered three major ML features with supporting maintenance work, improving experimentation speed, reproducibility, and cross-framework capabilities. Business value centers on rapid prototyping, robust evaluation, and GPU-accelerated workflows.
Activity
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Quality Metrics
Correctness88.4%
Maintainability86.6%
Architecture86.6%
Performance76.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
CNNComputer VisionData PreprocessingData ScienceData VisualizationDeep LearningGenerative Adversarial Networks (GAN)Generative ModelsImage ClassificationJupyter NotebookMachine LearningModel EvaluationModel TrainingObject DetectionPascal VOC Dataset
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