I already have a functioning image-based object detection pipeline and a clear set of evaluation metrics. What I need now is a precision-oriented overhaul. You’ll start from my existing repository and the accompanying documentation, then plug in three accuracy-boosting modules: • Better detection algorithms – swap in or layer on a stronger backbone/head while keeping the current training loop intact. • Enhanced preprocessing – introduce smarter augmentation or normalization steps so the model sees cleaner, more diverse input. • Advanced post-processing – refine NMS or related filtering so final predictions score higher under my mAP criteria. I will share the code, the change log, and the benchmark sheet that shows today’s baseline. Your deliverable is the updated codebase plus a short report that compares before-and-after results using the same test set. Clean, modular commits and comments are important—this project will be handed over to other engineers later. If you’ve deployed similar accuracy lifts on image detectors before, especially by combining algorithm tweaks with data and post-processing tricks, you’re the partner I’m after.