Comparing with enormous research achievements targeting better image classification models, efforts applied to object detector training are dwarfed in terms of popularity and universality. Due to significantly more complex network structures and optimization targets, various training strategies and pipelines are specifically designed for certain detection algorithms and no other. In this work, we explore universal tweaks that help boosting the performance of state-of-the-art object detection models to a new level without sacrificing inference speed. Our experiments indicate that these freebies can be as much as 5% absolute precision increase that everyone should consider applying to object detection training to a certain degree.
翻译:与针对更好的图像分类模型的巨大研究成果相比,在物体探测器培训方面所作的努力在普及性和普及性方面相形见绌。由于网络结构和优化目标更为复杂,各种培训战略和管道专门为某些探测算法设计,而没有其他。在这项工作中,我们探索有助于将最先进的物体探测模型的性能提升到新水平而又不牺牲推断速度的通用节奏。我们的实验表明,这些自由比子可以达到5%的绝对精确度增长,而每个人都应考虑将这种增长应用于对物体探测培训的某种程度。