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DN-DETR:通过引入查询消噪来加速DETR培训

Feng Li, Hao Zhang, Shilong Liu

IEEE transactions on pattern analysis and machine intelligence

|November 29, 2023

概括

本研究介绍了一种无声化训练方法,通过稳定双边图匹配来加快DETR (检测转换器) 训练. 这种方法显著提高了合速度和物体检测性能.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

DETR (检测转换器) 和类似的方法由于在早期培训阶段不稳定的双部分图匹配而遭受缓慢的融合.
这种不稳定性导致了不一致的优化目标,阻碍了有效的模型训练.

研究的目的:

开发一种新的Denoising培训方法,以加快DETR培训.
解决DETR类物体检测模型中的缓慢收问题.
为了更深入地了解基于变压器的探测器的融合挑战.

主要方法:

引入了一项否定的培训策略,以补充匈牙利的损失.
给变压器解码器输入杂的地面真相边界盒,以训练原始盒子的重建.
该方法旨在在DETR类架构中普遍适用,代码更改最小.

主要成果:

拟议的DN-DETR方法在相同的设置下实现了+1.9 AP的改进.
在12个和50个时代后,与ResNet-50骨干实现了46.0 AP和49.5 AP,分别.
使用50%更少的培训时代,证明了与基线方法可比的性能.

结论:

拒绝训练有效地减少了双边图匹配的困难,导致更快的融合.
该方法在物体检测准确度和训练效率方面取得了显著的改善.
在各种架构中验证了无声化培训的有效性,包括基于CNN的探测器和细分模型.

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