
Over a two-month period, contributed to the pskcci/DX-01 repository by developing an end-to-end real-time perception pipeline that integrates OpenVINO-based pose estimation, MiDaS depth sensing, and YOLOv5 object detection to identify when a beverage is near a detected hand. The work included building foundational documentation, educational scripts for data manipulation and image processing, and machine learning tutorials using Python and TensorFlow. Enhanced repository maintainability by removing large binary datasets and improving error handling. Delivered clear documentation and demo assets, enabling rapid onboarding and stakeholder feedback while demonstrating skills in computer vision, deep learning, and real-time system design.
December 2024 performance for pskcci/DX-01 focused on delivering an end-to-end real-time perception pipeline and refreshing project documentation/assets to accelerate demos, stakeholder reviews, and handover. The work established a live analytics loop with visualization improvements and a ready-to-demo documentation package, enhancing user experience and enabling faster feedback cycles.
December 2024 performance for pskcci/DX-01 focused on delivering an end-to-end real-time perception pipeline and refreshing project documentation/assets to accelerate demos, stakeholder reviews, and handover. The work established a live analytics loop with visualization improvements and a ready-to-demo documentation package, enhancing user experience and enabling faster feedback cycles.
November 2024 performance summary for pskcci/DX-01 focusing on business value, maintainability, and technical progress. Delivered foundational documentation scaffolding, data manipulation and image processing scripts, ML tutorials, and repository hygiene improvements. These changes establish a solid onboarding baseline, enable experimentation with ML and image processing, and reduce maintenance overhead by removing large binary datasets.
November 2024 performance summary for pskcci/DX-01 focusing on business value, maintainability, and technical progress. Delivered foundational documentation scaffolding, data manipulation and image processing scripts, ML tutorials, and repository hygiene improvements. These changes establish a solid onboarding baseline, enable experimentation with ML and image processing, and reduce maintenance overhead by removing large binary datasets.

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