rt-detr策略网络尺度的相关内容

文章 2025-02-09 来自：开发者社区

RT-DETR改进策略【模型轻量化】| 替换骨干网络为 ICCV 2023的EfficientViT 用于高分辨率密集预测的多尺度线性关注

一、本文介绍本文记录的是基于EfficientViT的RT-DETR轻量化改进方法研究。EfficientViT通过构建多尺度线性注意力模块将全局感受野与多尺度学习相结合，并以此模块为核心构建网络，构建轻量级且硬件高效的操作，以提升性能并降低硬件部署难度。本文在替换骨干网络中配置了原论文中的EfficientViT_M0、EfficientViT_M1、EfficientViT_M2、Eff....

文章 2025-02-08 来自：开发者社区

RT-DETR改进策略【Backbone/主干网络】| 替换骨干网络为CVPR-2024 PKINet 获取多尺度纹理特征，适应尺度变化大的目标

一、本文介绍本文记录的是利用PKINet优化RT-DETR的目标检测方法研究。在遥感图像目标检测中，目标尺度变化大，本文引入PKINet来捕获多尺度纹理特征，并在RT-DETR的基础上配置了原论文中PKINET_T', 'PKINET_S, PKINET_B三种模型，以满足不同的需求。专栏目录：RT-DETR改进目录一览 | 涉及卷积层、轻量化、注意力、损失函数、Backbone、SPPF.....

文章 2025-02-08 来自：开发者社区

RT-DETR改进策略【Backbone/主干网络】| 替换骨干网络为：Swin Transformer，提高多尺度特征提取能力

一、本文介绍本文记录的是基于SwinTransformer的RT-DETR目标检测改进方法研究。本文利用SwinTransformer替换RT-DETR的骨干网络，Swin Transformer的作用在于同时包含层次化特征表示和基于移位窗口的自注意力机制，克服了常见 Transformer模型在视觉任务中面临的尺度差异和分辨率差异问题。本文将其应用到RT-DETR中，使其在保持推理高效的同时....

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更新时间 2025-02-10 13:44:43

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