Le Tran 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. Their work included building environment scaffolding, acquiring datasets, and creating Jupyter notebooks for training and result visualization. Using Python and PyTorch, Le established a reproducible workflow that streamlines model evaluation and iteration. The project emphasized repository hygiene by removing outdated artifacts and updating documentation, resulting in a stable, production-friendly pipeline. This approach enabled rapid experimentation with new model variants, supporting efficient model selection and advancing the team’s computer vision capabilities.
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 No customer-impact bugs were reported this month; minor maintenance included cleanup of outdated notebooks (e.g., removal of 02_object_detection.ipynb) to improve stability and reproducibility.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 No customer-impact bugs were reported this month; minor maintenance included cleanup of outdated notebooks (e.g., removal of 02_object_detection.ipynb) to improve stability and reproducibility.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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