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RT-DETR 检测结果 AX650N 运行 RT-DETR 耗时 ＜10ms，完全满足实时运行需求。 同时 后处理（post process）耗时只有 0.17ms，极大的 降低 了 CPU Loading。 结束语 随着 Vision Transformer 网络模型的快速发展，越来越多有趣的AI应用将逐渐从云端服务迁移到边缘侧设备和端侧设备。 根据您提供的参考内容中的信息，当使用DETR训练自己的数据集而不采用预训练权重时，平均精度 (AP)保持在0的情况可能是由以下几个因素导致的： 一、快速回顾detr 1.1部分将快速回顾detr整体架构（会附上一些原始论文没有的，来自源码的细节）。 1.2部分需要重点关注，它将有助于我们对deformable detr的具象化理解。 1.1 detr整体架构 DETR架构图如下（原图来自DETR论文，为了讲解方便，我手动添加了一些细节）：
detr query解析 detr的object queries主要是用来查询是否存在目标以及目标位置的。类似cnn检测中的rpn，用来产生候选框。 在detr中，object queries为 (100, b, 256) 的可学习的参数，其中每个256维的向量代表了检测的box信息，这个信息是由类别和空间信息（box坐标）组成，其中类别信息用于区别类别，而空间信息. DETR（detection transformer）收敛慢的原因是什么？ DETR在coco上需要训练300轮。 后续的工作deformable DETR和 efficient DETR都在object queries上做了改… 显示全部 关注者 19 被浏览 detr的 object queries 类似 faster-rcnn 里面的rpn生成region proposals。 rpn最后是通过 nms 消除多余的region proposals；detr为了避免nms所以通过和encoder提取的全局信息进行 匈牙利匹配 消除多余的object queries。
一、本文介绍 本文记录的是基于MobileNet V4的RT-DETR目标检测轻量化改进方法研究。 其中MobileViT块旨在以较少的参数对输入张量中的局部和全局信息进行建模，结合卷积与 Transformer 的优势，并实现有效的信息编码与融合，。
推理的话，一张 3090 完全够用；训练的话，标准配置是4卡，每张卡 batchsize =8，总batchsize=32，总共大概需要40GB显存。所以，至少需要两张24GB 3090，每张batchsize设置成为16，才可以根据标准配置进行训练。 阅读了论文之后对DETR不需要NMS这个特点比较懵懂，有没有大佬可以解释一下为什么DETR不需要NMS也能学习到…
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