A year-long project trained traffic-sign detectors using only synthetic rendered images, achieving 75–84% of the performance of models trained on real photographs. Fine-tuning the synthetic-pretrained model on real data improved detection by 0.15 mAP50, with the largest gains on underrepresented sign classes, though modeling snow proved ineffective due to lack of real snowy conditions in test data.