在野外大规模的物体检测与不平衡的数据分布,和多标签.
概括
本研究解决了对象检测的开放图像数据集中的多标签和不平衡数据问题. 解决方案使单个模型的性能提高到60.90%的mAP,并使组合性能提高到67.17%的mAP.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习的性能在很大程度上依赖于训练数据量.
- 像Open Images这样的大规模数据集提供了机会,但也带来了挑战.
- 开放图像数据集由于其收集方法而遭受标签噪音和类不平衡.
研究的目的:
- 在大型物体检测数据集中对标签相关问题进行定量分析.
- 为多标签和不平衡数据问题提出和展示有效的解决方案.
- 在Open Images对象检测基准上提升最先进的性能.
主要方法:
- 开发了一个并发的softmax方法来管理多标签对象检测.
- 实施软平衡采样方法与混合培训计划器相结合,以解决标签不平衡问题.
- 设计了一个集体机制,以进一步提高单一模型的性能.
主要成果:
- 在开放图像测试组中,单个模型的平均平均精度 (mAP) 达到60.90%,提高了3.34分.
- 整体模型的整体mAP达到了67.17%,超过了之前的基准4.29点.
- 通过有效解决标签多样性和分配不平衡,证明了显著的绩效增长.
结论:
- 拟议的并发软max和软平衡采样有效地解决了大规模物体检测方面的重大挑战.
- 集合方法提供了实质性的性能提升,在开放图像数据集上设置了新的最先进的结果.
- 这项工作提供了一个全面的解决方案,用于改进在复杂,大规模数据集上训练的深度学习模型.
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