仅用合成数据训练检测汤罐的YOLOv11，实现91%准确率。
Synthetic-to-Real Object Detection using YOLOv11 and Domain Randomization Strategies
- 用多样合成数据和随机化增强提升模型泛化能力
- 最终在真实测试集上达到mAP@50 0.910
- 适合研究合成数据训练与域适应的开发者
本文针对物体检测中的合成到真实域差异问题，专注于仅使用合成数据与域随机化策略训练YOLOv11模型以检测特定物体（汤罐）。方法包括对数据增强、数据集构成和模型规模的广泛实验。尽管合成验证指标持续较高，但其与真实世界性能相关性差。因此，模型同时通过视觉检查与手动标注的真实测试集进行定性和定量评估。最终结果由Kaggle竞赛官方提供。关键发现表明，增加合成数据多样性（如不同视角和复杂背景）并配合精细调优的数据增强，是缩小域差距的关键。最佳配置为在扩展且多样的数据集上训练的YOLOv11l模型，在竞赛隐藏测试集上达到mAP@50 0.910。该结果展示了纯合成数据训练的潜力，也揭示了完全捕捉真实世界变化的挑战。
原文摘要 · Abstract (English)
This paper addresses the synthetic-to-real domain gap in object detection, focusing on training a YOLOv11 model to detect a specific object (a soup can) using only synthetic data and domain randomization strategies. The methodology involves extensive experimentation with data augmentation, dataset composition, and model scaling. While synthetic validation metrics were consistently high, they proved to be poor predictors of real-world performance. Consequently, models were also evaluated qualitatively, through visual inspection of predictions, and quantitatively, on a manually labeled real-world test set, to guide development. Final mAP@50 scores were provided by the official Kaggle competition. Key findings indicate that increasing synthetic dataset diversity, specifically by including varied perspectives and complex backgrounds, combined with carefully tuned data augmentation, were crucial in bridging the domain gap. The best performing configuration, a YOLOv11l model trained on an expanded and diverse dataset, achieved a final mAP@50 of 0.910 on the competition's hidden test set. This result demonstrates the potential of a synthetic-only training approach while also highlighting the remaining challenges in fully capturing real-world variability.
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