Preface:本篇文章主要讲解YOLO Pose(based on yolov5-5.0),论文选自CVPR2022: 《YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss》项目地址: GitHub - Tex…
论文地址: CVPR 2022 Open Access Repository 尝试一种很新的玩法,总分总结构写一下Loss拆解。 总: Ltotal代表所有的损失,λ为损失函数的加权,cls代表分类、box代表框、kpts代表关键点、kpts_conf代表关键点的置信度。 其中Lcls=BCELoss,Lbox=1−CIoU. Lkpts= Lkpts_conf= 除此之外,代码部分还有一个Lobj,即...
Ultralytics YOLO11是一款尖端的、最先进的模型,它在之前YOLO版本成功的基础上进行了构建,并引入了新功能和改进,以进一步提升性能和灵活性。YOLO11设计快速、准确且易于使用,使其成为各种物体检测和跟踪、实例分割、图像分类以及姿态估计任务的绝佳选择。 添加描述 pose官方在COCO数据集上做了更多测试: 添加描述 结构图...
This repository is the official implementation of the paper "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss" , accepted at Deep Learning for Efficient Computer Vision (ECV) workshop at CVPR 2022. This repository contains YOLOv5 based models for hum...
基于YOLO的3D目标检测:YOLO-6D This research project implements a real-time object detection and pose estimation method as described in the paper, Tekin et al. "Real-Time Seamless Single Shot 6D Object Pose Prediction", CVPR 2018. (https://arxiv.org/abs/
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https://github.com/ultralytics/yolov3 https://github.com/ultralytics/yolov5 https://github.com/DingXiaoH/RepVGG https://github.com/JUGGHM/OREPA_CVPR2022 https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose https://github.com/ultralytics/ultralyticsAbout...
This repository is the official implementation of the paper "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss" , accepted at Deep Learning for Efficient Computer Vision (ECV) workshop at CVPR 2022. This repository contains YOLOv5 based models for hum...
In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019. [Google Scholar] Chen, Y.; Tian, Y.; He, M. Monocular human pose estimation: A survey of deep learning-based methods. Comput. Vis. Image Underst. 2020, 192, ...
Bugra Tekin, Sudipta N. Sinha and Pascal Fua, "Real-Time Seamless Single Shot 6D Object Pose Prediction", CVPR 2018. Introduction We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to ...