Cornell-University-Combat-Robotics/Autonomous-24-25
Nov 2024 – Dec 2024
2 Months active
Languages Used
Git AttributesPython
Technical Skills
API IntegrationComputer VisionData LoadingDeep LearningMachine LearningModel Training
empzhang22
PROFILE
Empzhang22
Developed and enhanced the core robotic perception pipeline for the Cornell-University-Combat-Robotics/Autonomous-24-25 repository, focusing on end-to-end model training, inference, and dataset management. Leveraged Python and PyTorch to implement a unified model interface supporting YOLO-based object detection, automated prediction testing, and robust data loading. Integrated homography warp utilities and established a dedicated training dataset to streamline perception model evaluation and reproducibility. Improved repository hygiene by refining version control practices and excluding environment-specific artifacts. The work emphasized modularity, extensibility, and deployment readiness, enabling efficient development and testing of computer vision models within a collaborative research environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
6
Lines of code
4,142
Activity Months2
Your Network
157 peopleSame Organization
@cornell.edu144
Work History
December 2024
3 Commits • 3 Features
Dec 1, 2024December 2024 — Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered foundational enhancements across perception testing, data infrastructure, and geometric warping utilities, establishing end-to-end readiness for model training and evaluation. Focused on enabling automated testing for YOLO, provisioning a ready-to-use training dataset, and implementing robust homography warp utilities to support visual perception pipelines. These changes improve testing throughput, data quality, and deployment readiness for perception models.
3 Commits • 3 Features
Dec 1, 2024December 2024 — Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered foundational enhancements across perception testing, data infrastructure, and geometric warping utilities, establishing end-to-end readiness for model training and evaluation. Focused on enabling automated testing for YOLO, provisioning a ready-to-use training dataset, and implementing robust homography warp utilities to support visual perception pipelines. These changes improve testing throughput, data quality, and deployment readiness for perception models.
December 2024
November 2024
8 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focusing on end-to-end perception pipeline, YOLO-based predictions, and repository hygiene; emphasizes business value and technical achievements; prepared for production deployment and reproducibility.
November 2024
8 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focusing on end-to-end perception pipeline, YOLO-based predictions, and repository hygiene; emphasizes business value and technical achievements; prepared for production deployment and reproducibility.
Activity
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Quality Metrics
Correctness82.6%
Maintainability84.6%
Architecture80.8%
Performance72.6%
AI Usage21.8%
Skills & Technologies
Programming Languages
Git AttributesPython
Technical Skills
API IntegrationComputer VisionData EngineeringData LoadingDataset ManagementDeep LearningImage ProcessingMachine LearningModel TrainingObject DetectionObject-Oriented ProgrammingPyTorchPythonPython ScriptingScripting
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