natmourajr/CPE883-2025-02
Sep 2025 – Sep 2025
1 Month active
Languages Used
DockerfilePythonShell
Technical Skills
C++ DevelopmentComputer VisionConfiguration ManagementData LoadingData PreprocessingData Visualization
gabrielhblisboa
PROFILE
Gabrielhblisboa
Gabriel Lisboa developed the Sonar ML experimentation platform in the natmourajr/CPE883-2025-02 repository, focusing on reproducible machine learning workflows for sonar data. He designed and implemented a comprehensive data loading pipeline and introduced new model architectures, including initial versions of CapsNet and ViT, to support rapid experimentation and comparison. Gabriel leveraged Python and PyTorch for model development and integrated Docker to ensure consistent, shareable environments. His work emphasized structured experimentation through versioned configuration files and robust evaluation utilities, including confusion matrices and sonar-specific metrics, laying a solid foundation for scalable research and efficient validation of new approaches in computer vision.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
3,363
Activity Months1
Your Network
16 peopleSame Organization
@lps.ufrj.br2
Work History
September 2025
5 Commits • 1 Features
Sep 1, 2025In Sep 2025, delivered the Sonar ML experimentation platform within natmourajr/CPE883-2025-02, establishing a reproducible end-to-end workflow for loading data, training diverse architectures, configuring experiments, and evaluating results with sonar-specific metrics. The month focused on enabling structured experimentation, versioned configurations, and robust evaluation while laying groundwork for scalability and rapid validation of new approaches.
5 Commits • 1 Features
Sep 1, 2025In Sep 2025, delivered the Sonar ML experimentation platform within natmourajr/CPE883-2025-02, establishing a reproducible end-to-end workflow for loading data, training diverse architectures, configuring experiments, and evaluating results with sonar-specific metrics. The month focused on enabling structured experimentation, versioned configurations, and robust evaluation while laying groundwork for scalability and rapid validation of new approaches.
September 2025
Activity
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Quality Metrics
Correctness82.0%
Maintainability80.0%
Architecture82.0%
Performance64.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfilePythonShell
Technical Skills
C++ DevelopmentComputer VisionConfiguration ManagementData LoadingData PreprocessingData VisualizationDeep LearningDockerExperimentationJupyterLabMachine LearningModel DeploymentModel DevelopmentPyTorchPython Development
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