aidotse/LeakPro
Dec 2024 – Mar 2026
11 Months active
Languages Used
PythonJupyter NotebookShellYAMLJinjaC++None
Technical Skills
Code LintingData AugmentationData PrivacyDeep LearningFederated LearningImage Processing
viktorvaladi
PROFILE
Viktorvaladi
Viktor Valadi developed core privacy-preserving machine learning features and robust data pipelines for the aidotse/LeakPro repository, focusing on object detection, adversarial evaluation, and gradient inversion attacks. He engineered end-to-end workflows using Python and PyTorch, integrating COCO and CelebA datasets, YOLO and ResNet architectures, and Optuna-based hyperparameter tuning to accelerate experimentation and improve reproducibility. His work included architectural refactoring, code quality improvements, and CI/CD automation, ensuring maintainability and reliable deployment. By addressing data privacy, model robustness, and evaluation flexibility, Viktor enabled faster iteration cycles and more secure, production-ready ML workflows, demonstrating depth in deep learning and software engineering practices.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
101Total
Bugs
17
Commits
101
Features
35
Lines of code
2,582,021
Activity Months11
Your Network
26 peopleWork History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for aidotse/LeakPro focusing on delivering foundational engineering scaffolding and code quality improvements that enable faster releases and maintainability.
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for aidotse/LeakPro focusing on delivering foundational engineering scaffolding and code quality improvements that enable faster releases and maintainability.
March 2026
January 2026
3 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for aidotse/LeakPro: Delivered configurable gradient inversion attack enhancements and improved code maintainability. Key feature deliverables include enhancements to the Gradient Inversion Attack (GI) with improved similarity score handling, new hooks for layer normalization, code refactors for better organization, and updated configuration management for attack parameters, plus GIBase enhancements to improve configurability of the norm-estimate attack. In parallel, linting and readability improvements across the repository were completed without changing functionality, reducing technical debt and improving long-term maintainability. These efforts enhance evaluation workflows, accelerate experimentation, and support more reliable security assessments.
January 2026
3 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for aidotse/LeakPro: Delivered configurable gradient inversion attack enhancements and improved code maintainability. Key feature deliverables include enhancements to the Gradient Inversion Attack (GI) with improved similarity score handling, new hooks for layer normalization, code refactors for better organization, and updated configuration management for attack parameters, plus GIBase enhancements to improve configurability of the norm-estimate attack. In parallel, linting and readability improvements across the repository were completed without changing functionality, reducing technical debt and improving long-term maintainability. These efforts enhance evaluation workflows, accelerate experimentation, and support more reliable security assessments.
August 2025
2 Commits • 2 Features
Aug 1, 2025August 2025 performance summary for aidotse/LeakPro: Delivered two key enhancements focused on performance and maintainability. YOLO Training Hyperparameter Optimization tuned training hyperparameters to improve model performance and training efficiency, enabling faster iterations and better readiness for deployment. GIABase Constructor Linting and Code Quality Improvement resolved linting issues in the GIABase constructor, reducing technical debt and improving code quality and maintainability. No critical defects were reported this month; efforts were focused on robust feature delivery and code health. Overall impact: enhanced model performance, accelerated development cycles, and a cleaner, more maintainable codebase. Technologies/skills demonstrated: Python, ML/DL model tuning with YOLO, training pipelines, linting and static analysis, code quality practices, and Git traceability.
2 Commits • 2 Features
Aug 1, 2025August 2025 performance summary for aidotse/LeakPro: Delivered two key enhancements focused on performance and maintainability. YOLO Training Hyperparameter Optimization tuned training hyperparameters to improve model performance and training efficiency, enabling faster iterations and better readiness for deployment. GIABase Constructor Linting and Code Quality Improvement resolved linting issues in the GIABase constructor, reducing technical debt and improving code quality and maintainability. No critical defects were reported this month; efforts were focused on robust feature delivery and code health. Overall impact: enhanced model performance, accelerated development cycles, and a cleaner, more maintainable codebase. Technologies/skills demonstrated: Python, ML/DL model tuning with YOLO, training pipelines, linting and static analysis, code quality practices, and Git traceability.
August 2025
July 2025
18 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for aidotse/LeakPro: Delivered a robust data pipeline and model enhancements, improved attack data handling, and completed essential maintenance to boost reliability, experiment reproducibility, and business value.
July 2025
18 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for aidotse/LeakPro: Delivered a robust data pipeline and model enhancements, improved attack data handling, and completed essential maintenance to boost reliability, experiment reproducibility, and business value.
June 2025
31 Commits • 8 Features
Jun 1, 2025June 2025 for aidotse/LeakPro: Delivered robust ML experimentation and stability improvements, facilitating faster, safer iteration and easier adoption. Key features include Optuna data-points support with a tuning workflow, YOLO model integration enabling a YOLO-based object-detection workflow, addition of image saving for result persistence, Gia optimizer text enhancements, and README documentation updates with usage notes and examples. Major bugs fixed span Optuna PII handling to prevent data leakage, CIFAR-100 and Huang16 example fixes, Run.py bug fix, and general stabilization with dependency and torch version alignment. Impact: improved reproducibility, clearer data provenance, and a smoother path to production-ready workflows. Technologies demonstrated: Python, Optuna, PyTorch, image I/O, linting and code quality improvements, and thorough documentation.
31 Commits • 8 Features
Jun 1, 2025June 2025 for aidotse/LeakPro: Delivered robust ML experimentation and stability improvements, facilitating faster, safer iteration and easier adoption. Key features include Optuna data-points support with a tuning workflow, YOLO model integration enabling a YOLO-based object-detection workflow, addition of image saving for result persistence, Gia optimizer text enhancements, and README documentation updates with usage notes and examples. Major bugs fixed span Optuna PII handling to prevent data leakage, CIFAR-100 and Huang16 example fixes, Run.py bug fix, and general stabilization with dependency and torch version alignment. Impact: improved reproducibility, clearer data provenance, and a smoother path to production-ready workflows. Technologies demonstrated: Python, Optuna, PyTorch, image I/O, linting and code quality improvements, and thorough documentation.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for aidotse/LeakPro: Focused on advancing the YOLO-based detection pipeline and setting up automated experimentation to improve model performance and reliability. Key work includes initiating YOLO model enhancements with an Optuna-based hyperparameter tuning workflow, alongside data loading improvements for new image formats, refined training configurations, and added debugging features to support robust training. This groundwork aims to accelerate iteration, improve detection accuracy, and reduce downtime during model training.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for aidotse/LeakPro: Focused on advancing the YOLO-based detection pipeline and setting up automated experimentation to improve model performance and reliability. Key work includes initiating YOLO model enhancements with an Optuna-based hyperparameter tuning workflow, alongside data loading improvements for new image formats, refined training configurations, and added debugging features to support robust training. This groundwork aims to accelerate iteration, improve detection accuracy, and reduce downtime during model training.
April 2025
5 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for aidotse/LeakPro. Delivered targeted refactors, dataset handling enhancements, COCO integration with advanced augmentations, and a critical training metric tracking bug fix. These changes improve maintainability, data integrity, experimentation speed, and model performance visibility, delivering measurable business value in reliability and deployment readiness.
5 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for aidotse/LeakPro. Delivered targeted refactors, dataset handling enhancements, COCO integration with advanced augmentations, and a critical training metric tracking bug fix. These changes improve maintainability, data integrity, experimentation speed, and model performance visibility, delivering measurable business value in reliability and deployment readiness.
April 2025
March 2025
6 Commits • 3 Features
Mar 1, 2025March 2025 monthly recap for aidotse/LeakPro. Focused on delivering core dataset and evaluation capabilities, tightening code quality, and stabilizing the end-to-end training/evaluation pipeline. This period catalyzed deeper experimentation with robust object-detection data loading, more flexible attack configurations, and a maintainable codebase that enables faster iteration and clearer business value.
March 2025
6 Commits • 3 Features
Mar 1, 2025March 2025 monthly recap for aidotse/LeakPro. Focused on delivering core dataset and evaluation capabilities, tightening code quality, and stabilizing the end-to-end training/evaluation pipeline. This period catalyzed deeper experimentation with robust object-detection data loading, more flexible attack configurations, and a maintainable codebase that enables faster iteration and clearer business value.
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025—LeakPro feature delivery and data integration. Implemented initial YOLOv8 support and COCO evaluation workflow within LeakPro, enabling end-to-end evaluation of YOLOv8 models. Extended GIA data modality system and migrated GiaImageDetectionExtension to HuangConfig to improve dataset compatibility across configurations. No major bugs fixed this month; primary focus was establishing a robust evaluation pipeline and broader dataset support to accelerate model experiments and potential deployment readiness. Business value includes faster iteration on object detection models, improved dataset interoperability, and a more configurable, scalable evaluation workflow.
4 Commits • 2 Features
Feb 1, 2025February 2025—LeakPro feature delivery and data integration. Implemented initial YOLOv8 support and COCO evaluation workflow within LeakPro, enabling end-to-end evaluation of YOLOv8 models. Extended GIA data modality system and migrated GiaImageDetectionExtension to HuangConfig to improve dataset compatibility across configurations. No major bugs fixed this month; primary focus was establishing a robust evaluation pipeline and broader dataset support to accelerate model experiments and potential deployment readiness. Business value includes faster iteration on object detection models, improved dataset interoperability, and a more configurable, scalable evaluation workflow.
February 2025
January 2025
23 Commits • 9 Features
Jan 1, 2025January 2025 — aidotse/LeakPro: Delivered core features, architectural improvements, and strengthened quality controls to accelerate reliable experimentation and reduce risk. Key outcomes include initial Huang Princeton integration, pyproject-based test configuration, expanded Optuna support with seed-based reproducibility, and new abstraction layer plus GIAs-based abstractions; plus broad linting and code quality fixes and reviewer-driven improvements. These changes reduce onboarding friction, stabilize core flows, and enable faster iteration cycles for model experiments and configuration management.
January 2025
23 Commits • 9 Features
Jan 1, 2025January 2025 — aidotse/LeakPro: Delivered core features, architectural improvements, and strengthened quality controls to accelerate reliable experimentation and reduce risk. Key outcomes include initial Huang Princeton integration, pyproject-based test configuration, expanded Optuna support with seed-based reproducibility, and new abstraction layer plus GIAs-based abstractions; plus broad linting and code quality fixes and reviewer-driven improvements. These changes reduce onboarding friction, stabilize core flows, and enable faster iteration cycles for model experiments and configuration management.
December 2024
6 Commits • 2 Features
Dec 1, 2024December 2024 (aidotse/LeakPro): Delivered end-to-end enhancements for privacy-analysis workflows. Key features include CelebA dataset support for the Gradient Inversion Attack (GIA) workflow, with data loading, pre-training, main execution scripts, and updated configurations to enable CelebA-based attacks; and the integration of the GradInversion attack into the LeakPro framework, adding ResNet model implementations and utilities for data preparation and attack execution to enable gradient reconstruction for privacy analysis. Code hygiene and quality improvements were completed, including lint fixes (reordering imports in invertinggradients.py) and removal of stray test/unused files. These efforts collectively extend the framework’s end-to-end capabilities for privacy research while maintaining maintainability and clean build pipelines.
6 Commits • 2 Features
Dec 1, 2024December 2024 (aidotse/LeakPro): Delivered end-to-end enhancements for privacy-analysis workflows. Key features include CelebA dataset support for the Gradient Inversion Attack (GIA) workflow, with data loading, pre-training, main execution scripts, and updated configurations to enable CelebA-based attacks; and the integration of the GradInversion attack into the LeakPro framework, adding ResNet model implementations and utilities for data preparation and attack execution to enable gradient reconstruction for privacy analysis. Code hygiene and quality improvements were completed, including lint fixes (reordering imports in invertinggradients.py) and removal of stray test/unused files. These efforts collectively extend the framework’s end-to-end capabilities for privacy research while maintaining maintainability and clean build pipelines.
December 2024
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Quality Metrics
Correctness86.6%
Maintainability86.4%
Architecture86.0%
Performance81.2%
AI Usage30.2%
Skills & Technologies
Programming Languages
C++JinjaJupyter NotebookNonePythonShellYAML
Technical Skills
AI model trainingBug FixCI/CDCOCO DatasetCode FormattingCode LintingCode OrganizationCode QualityCode RefactoringCode quality improvementCode refactoringComputer VisionData AnalysisData AugmentationData Loading
Repositories Contributed To
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