Computer Vision and LaTeX Expert

I have a proprietary image dataset and I need a seasoned computer-vision engineer to turn it into an end-to-end solution that can 1) recognise each image, 2) detect all relevant objects inside it, and 3) optionally segment those objects when that adds value to the overall result. The primary goal is accurate detection and classification, but I’d like the system to be flexible enough to switch on pixel-level segmentation whenever it boosts performance.

Here is what I’m after:
• A clean Python pipeline (PyTorch or TensorFlow preferred, with OpenCV for preprocessing) that ingests my custom images, trains suitable models, and exposes an easy inference script.
• Clear evaluation: confusion matrices, mAP/IoU scores, and plots of loss/accuracy over epochs so I can judge progress at a glance.
• A concise, publication-ready report written in LaTeX. I will supply the template; you’ll fill in methodology, equations, hyper-parameters, and results, then generate the final PDF.

I will provide the raw images, any existing labels, and baseline ideas. You return:
1. Source code (well commented).
2. Trained weights and instructions for retraining on fresh data.
3. The LaTeX source and compiled PDF.

Accuracy, reproducibility, and tidy technical writing are my key acceptance criteria. If you have recently shipped similar object-detection or segmentation projects and are comfortable typesetting detailed equations in LaTeX, I’m ready to get started right away.