In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet-like / CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of "large neck, small head". We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results. In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios, i.e., DAMO-YOLO-Tiny/Small/Medium. They can achieve 43.0/46.8/50.0 mAPs on COCO with the latency of 2.78/3.83/5.62 ms on T4 GPUs respectively. The code is available at https://github.com/tinyvision/damo-yolo.
翻译:在本报告中,我们展示了一种称为DAMO-YOLO的快速和准确的物体探测方法,该方法的性能优于先进的YOLO系列。DAMO-YOLO从YOLO得到一些新技术的扩展,包括神经结构搜索(NAS),高效的再校准通用-FPN(REpGPN),一个配有Agest OTA标签任务的轻量级头部,以及蒸馏增强。特别是,我们使用MAE-NAS这一以最大摄氏度原则为指导的方法,在低延度和高性能的限制下搜索我们的探测骨干。DAMO-YOLOLO(YNet-CSP)类似结构,配有空间金字塔集合和焦点模块。在设计颈部和头部时,我们采用“大颈部,小头部”的规则。我们进口通用的FAFN,配有加速的电磁架,配有高效的层集成网络/再校准。然后,我们调查探测器头大小如何影响检测性工作,发现高部颈部一级,只有一个任务层的SDOMODLA 将产生更好的业绩。这些升级。