Watch:Real-Time Detection Transformer (RT-DETR) Overview of Baidu's RT-DETR.The RT-DETR model architecture diagram shows the last three stages of the backbone {S3, S4, S5} as the input to the encoder. The efficient hybrid encoder transforms multiscale features into a sequence of image featu...
这个源码只写了'backbone'(骨干), 'encoder'(编码器), 'decoder'(解码器) rtdetr.py """by lyuwenyu """importtorchimporttorch.nnasnnimporttorch.nn.functionalasFimportrandomimportnumpyasnpfromsrc.coreimportregister __all__ = ['RTDETR', ]@registerclassRTDETR(nn.Module):#依赖注入'backbone', 'en...
DETRs Beat YOLOs on Real-time Object Detection DETR 在实时目标检测方面超越 YOLO 论文链接：https://volctracer.com/w/hVXY8LNc 论文作者 Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, Jie Chen 内容简介 该论文提出了一种名为Real-Time DEtection ...
后来DETR-style检测框架就不需要NMS了。目前实时检测框架都是单阶段的,均要求NMS后处理,但是NMS并不好优化,因此考虑将DETR-style检测器实时化。但是DETR-style检测器,尤其是使用多尺度特征的encoder阶段,其self-attention操作是features length的平方倍计算复杂度,所以本文想要解决这个问题,提出RT-DETR。 Preliminaries: ...
To make the model more lightweight, we propose a Real-Time DEtection TRansformer with Bi-Level Routing Attention (RDETR-BRA). Specifically, we incorporated an efficient hybrid encoder based on bi-level routing attention, which efficiently processes multi-scale features by decoupling intra scale ...
该论文提出了一种名为Real-Time DEtection TRansformer(RT-DETR)的新型实时目标检测框架,据称是首个能够在速度和准确性上同时超越现有先进YOLO检测器的端到端对象检测器。RT-DETR通过设计高效的混合编码器和不确定性最小的查询选择机制,显著提高了检测速度和准确性,并支持通过调整解码器层数来灵活调整检测速度,以适应...
In this article we introduce Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector addressing the issue of the high computational cost existing with the DETRs. The recent research paper DETRs Beat YOLOs on Real-Time Object Detection, a Baidu Inc., success...
Therefore, a real-time end-to-end high-resolution remote sensing object detection method based on RT-DETR (CDE-DETR) is proposed. Through introducing cascaded group attention, we propose CGA-IFI for intra-scale feature interaction. The DRB-CFFM is designed with a dilated reparam block to ...
This is because there are NaN values produced in training process. The rtdetr training withamp=Trueis more sensitive than yolo models to custom datasets, probably it's related to the transformer architecture and nms-free design(we might figure out a better solution for this in the future). ...
For example, our YOLOv10-S is 1.8 × imes faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8 × imes smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance. 展开...