Fine-Tune Vehicle Detection Model

I have a fully-labeled dataset containing vehicles and their licence plates and now need it to power a production-ready object-detection model. I’m flexible on the backbone—you can work with YOLO, SSD, Faster R-CNN or whichever modern architecture you feel will squeeze out the best accuracy—but the end result must reliably spot both the vehicle and the plate in a single pass.

What I will provide
• Training, validation and test splits already annotated in COCO-style JSON
• A small script that shows the folder layout I use for images and labels

What I expect back
• The fine-tuned weights
• All training and evaluation scripts (Python preferred; PyTorch or TensorFlow are both fine)
• A quick-start readme so I can reproduce your results on my machine (GPU)
• Basic metrics report—precision, recall and mAP on the held-out test set

Acceptance criteria
1. Model reaches balanced precision/recall suitable for real-time inference (we can finalise the exact threshold together).
2. Inference script runs on a single GPU with clear instructions for CPU fallback.
3. Code is clean, modular and uses standard open-source libraries only.

If you have experience squeezing every last percent out of detection models and can hand back a tidy, reproducible solution, I’d love to see your approach and timeline.