Cornell-University-Combat-Robotics/Autonomous-24-25
Nov 2024 – Nov 2024
1 Month active
Languages Used
GitGit ConfigurationJupyter NotebookPython
Technical Skills
API IntegrationComputer VisionData EngineeringData LoadingDebuggingDeep Learning
halyssa
PROFILE
Halyssa
Andrew Hsu developed a robust CNN object detection training pipeline for the Cornell-University-Combat-Robotics/Autonomous-24-25 repository, focusing on scalable autonomous robotics workflows. He engineered multi-task loss functions, improved data loading, and managed datasets to enhance model accuracy and stability. Using Python and PyTorch, Andrew refined model architecture, tuned training hyperparameters, and integrated Roboflow for streamlined dataset handling. He also built an end-to-end prediction module for inference and visualization, supporting deployment with updated model artifacts. By addressing validation and batch processing bugs and maintaining repository hygiene with Git, Andrew delivered a reproducible, maintainable codebase that supports rapid model iteration and deployment.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
15Total
Bugs
1
Commits
15
Features
4
Lines of code
1,484
Activity Months1
Your Network
13 people
Work History
November 2024
15 Commits • 4 Features
Nov 1, 2024November 2024 for Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered a robust CNN object detection training pipeline with multi-task losses, robust data loading, dataset management, tuned training hyperparameters, architectural refinements, and Roboflow integration. Launched a Prediction Module for inference and visualization. Expanded model artifacts and dataset support (added most recent model and 400-dataset model). Fixed critical issues in training validation, dataset length calculations, and batch processing to stabilize training runs. Updated repository hygiene by refining gitignore to exclude runs and models. These efforts deliver improved detection accuracy, faster deployment of updated models, and a cleaner, reproducible codebase enabling scalable autonomous robotics workflows.
15 Commits • 4 Features
Nov 1, 2024November 2024 for Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered a robust CNN object detection training pipeline with multi-task losses, robust data loading, dataset management, tuned training hyperparameters, architectural refinements, and Roboflow integration. Launched a Prediction Module for inference and visualization. Expanded model artifacts and dataset support (added most recent model and 400-dataset model). Fixed critical issues in training validation, dataset length calculations, and batch processing to stabilize training runs. Updated repository hygiene by refining gitignore to exclude runs and models. These efforts deliver improved detection accuracy, faster deployment of updated models, and a cleaner, reproducible codebase enabling scalable autonomous robotics workflows.
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness86.0%
Maintainability85.4%
Architecture82.0%
Performance76.0%
AI Usage25.4%
Skills & Technologies
Programming Languages
GitGit ConfigurationJupyter NotebookPython
Technical Skills
API IntegrationComputer VisionData EngineeringData LoadingDebuggingDeep LearningMachine LearningModel ArchitectureModel RefactoringModel TrainingObject DetectionOpenCVPyTorchVersion Control
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline