Object Detection Model Finetuning

I need to fine-tune an existing computer-vision model for object detection so it performs reliably on my custom dataset. The base architecture is already selected; the task now is to prepare the data, set up transfer-learning, run hyper-parameter sweeps, and deliver a set of weights that reach an agreed-upon mAP on a held-out validation split. You may use PyTorch, TensorFlow, or another mainstream framework as long as the final model can be exported to ONNX.

Please share examples of past work where you have successfully fine-tuned or trained object-detection networks—links to repos, papers, or short demos are ideal. Once we decide on the starting checkpoint and training environment, I’ll supply labeled images, class definitions, and any additional annotations you need.

Deliverables:
• Training scripts and configuration files
• The fine-tuned model weights saved in their native format plus ONNX
• A brief report covering dataset prep, training settings, and evaluation metrics

I’ll review your past projects first, then we can lock down milestones and kick off the run.