AI Object Detection Automation

I need a streamlined AI workflow that scans large volumes of photographs and pinpoints objects with markedly higher accuracy than our current setup. The idea is to automate the entire path—from ingesting raw image files to returning precise bounding-box coordinates—so quality control no longer hinges on manual review.

Here is what matters most to me:

• A well-trained object-detection model tailored to photographic input (no illustrations or charts in the mix).
• Consistently higher precision and recall than my existing baseline; lowering false positives is more valuable than sheer speed.
• A self-contained script (Python preferred) that can run headless on Linux, making use of familiar libraries such as PyTorch, TensorFlow, or OpenCV—whatever you feel will maximise accuracy.
• A concise README that explains installation, inference commands, and how to tweak confidence thresholds.

I will supply an initial, labelled photo set for training and a separate, hidden validation set for final evaluation. If your model meets or exceeds the benchmark metrics on that blind test, the job is considered complete.