GHOST-Science-Club/tree-classification-irim
Feb 2025 – Apr 2025
3 Months active
Languages Used
PythonYAML
Technical Skills
Configuration ManagementData AugmentationData HandlingData PreprocessingHyperparameter TuningMachine Learning
Adam Mazur
PROFILE
Adam Mazur
Worked on the GHOST-Science-Club/tree-classification-irim repository, delivering a configurable and production-oriented machine learning training pipeline focused on imbalanced data. Developed dynamic data balancing strategies, including undersampling and oversampling, controlled via YAML configuration to enable rapid experimentation and reproducibility. Enhanced the pipeline with curriculum learning, class weighting, and hyperparameter tuning to improve minority-class performance and training stability. Addressed GPU compatibility and precision issues, optimizing for both speed and reliability across platforms. Used Python and PyTorch Lightning to implement backend improvements, bug fixes, and performance optimizations, resulting in a robust, maintainable workflow for data-driven model development and evaluation.
Overall Statistics
Feature vs Bugs
54%Features
Repository Contributions
33Total
Bugs
12
Commits
33
Features
14
Lines of code
531
Activity Months3
Your Network
5 peopleShared Repositories
5
Work History
April 2025
22 Commits • 10 Features
Apr 1, 2025April 2025 monthly summary for GHOST-Science-Club/tree-classification-irim focused on performance, stability, and configurability improvements in the training pipeline. The month delivered a mix of core feature work, targeted bug fixes, and backend cleanup that collectively enhance throughput, accuracy, and ease of experimentation across GPU platforms.
22 Commits • 10 Features
Apr 1, 2025April 2025 monthly summary for GHOST-Science-Club/tree-classification-irim focused on performance, stability, and configurability improvements in the training pipeline. The month delivered a mix of core feature work, targeted bug fixes, and backend cleanup that collectively enhance throughput, accuracy, and ease of experimentation across GPU platforms.
April 2025
March 2025
3 Commits • 2 Features
Mar 1, 2025March 2025: Delivered configurable data balancing in the tree-classification-irim training pipeline, enabling systematic testing of undersampling and oversampling strategies. Implemented config-driven dynamic selection between balancing methods and introduced oversampling with a defined threshold, laying groundwork for data-driven improvements on imbalanced datasets.
March 2025
3 Commits • 2 Features
Mar 1, 2025March 2025: Delivered configurable data balancing in the tree-classification-irim training pipeline, enabling systematic testing of undersampling and oversampling strategies. Implemented config-driven dynamic selection between balancing methods and introduced oversampling with a defined threshold, laying groundwork for data-driven improvements on imbalanced datasets.
February 2025
8 Commits • 2 Features
Feb 1, 2025Monthly Summary for 2025-02 (GHOST-Science-Club/tree-classification-irim): Delivered a balanced and configurable data handling and training workflow to improve model performance on imbalanced datasets, with a focus on reproducibility and business value. The work supports flexible experimentation and stable training dynamics in production-like training runs.
8 Commits • 2 Features
Feb 1, 2025Monthly Summary for 2025-02 (GHOST-Science-Club/tree-classification-irim): Delivered a balanced and configurable data handling and training workflow to improve model performance on imbalanced datasets, with a focus on reproducibility and business value. The work supports flexible experimentation and stable training dynamics in production-like training runs.
February 2025
Activity
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Quality Metrics
Correctness79.4%
Maintainability87.2%
Architecture76.4%
Performance66.6%
AI Usage21.2%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Backend DevelopmentBug FixConfiguration ManagementData AugmentationData EngineeringData HandlingData PreprocessingData ScienceDeep LearningGPU ComputingGPU OptimizationHyperparameter TuningMachine LearningMachine Learning ConfigurationModel Training
Repositories Contributed To
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