Developed an end-to-end object detection feature for the MHC-FA24-CS341CV/beyond-the-pixels-emerging-computer-vision-research-topics-fa24 repository, focusing on training and benchmarking models across YOLOv9, YOLOv10, and YOLOv11n. The work included building environment and configuration scaffolding, acquiring datasets, and creating Jupyter notebooks for training and result visualization. Python and PyTorch were used extensively to ensure reproducibility and facilitate rapid evaluation of new model variants. Documentation was updated and outdated artifacts were removed to maintain repository hygiene. This approach established a production-friendly workflow, enabling efficient model selection and iteration for computer vision tasks without introducing customer-impacting bugs.
November 2024 — Delivered an end-to-end object detection feature for the MHC-FA24 project, including training and benchmarking across YOLOv9, YOLOv10, and YOLOv11n. Implemented environment/configuration scaffolding, dataset acquisition, training notebooks, visualization of results, and updated documentation. No customer-impact bugs were reported this month; minor maintenance included cleanup of outdated notebooks (e.g., removal of 02_object_detection.ipynb) to improve stability and reproducibility. The work establishes a reproducible, production-friendly workflow that enables rapid evaluation of new model variants, accelerating model selection and iteration. Technologies demonstrated include Python, PyTorch, the YOLO family, Jupyter notebooks, data visualization, and environment management.
November 2024 — Delivered an end-to-end object detection feature for the MHC-FA24 project, including training and benchmarking across YOLOv9, YOLOv10, and YOLOv11n. Implemented environment/configuration scaffolding, dataset acquisition, training notebooks, visualization of results, and updated documentation. No customer-impact bugs were reported this month; minor maintenance included cleanup of outdated notebooks (e.g., removal of 02_object_detection.ipynb) to improve stability and reproducibility. The work establishes a reproducible, production-friendly workflow that enables rapid evaluation of new model variants, accelerating model selection and iteration. Technologies demonstrated include Python, PyTorch, the YOLO family, Jupyter notebooks, data visualization, and environment management.
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