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提出了一种新的基于最小点距离的边界框相似性比较指标MPDIoU,该指标包含了现有损失函数中考虑的所有相关因素,即重叠或不重叠区域、中心点距离、宽度和高度偏差,同时简化了计算过程。在此基础上,我们提出了基于MPDIoU的边界框回归损失函数,称为LMPDIoU。实验结果表明,MPDIoU损失函数应用于最先进的实例分割(如YOLACT)和基于PASCAL VOC、MS COCO和IIIT5k训练的目标检
Ipopthttps://github.com/coin-or/IpoptOverview - NLopt DocumentationNonehttps://nlopt.readthedocs.io/en/latest/【论文阅读1】SLAM++: SLAM at the Level of Objects - 知乎去年读了不少论文,但是感觉还是记录下来印象更深刻一些。就在知乎记录吧,内容基本都是是
一、ceres安装:1.Ctrl+Alt+T打开终端:安装依赖:sudo apt-get installliblapack-dev libsuitesparse-dev libcxsparse3 libgflags-dev libgoogle-glog-dev libgtest-dev2. 下载源码:https://github.com/ceres-solver/ceres-solverGitHu
安装好labelme工具 https://github.com/wkentaro/labelme一、安装在miniconda或者Anaconda环境里安装:pip install labelme-i https://pypi.douban.com/simple/pip install pyqt5-i https://pypi.douban.com/simple/pip install -i htt
No More Strided Convolutions or Pooling:A New CNN Building Block for Low-ResolutionImages and Small Objects 无卷积步长或池化:用于低分辨率图像和小物体的新 CNN 模块SPD-Conv提出了一个名为SPD-Conv的新的CNN构建块,它完全消除了步长和池化操作,取而代之的是一个空间到深度卷积
result = {**result, **{train_no: np.zeros(tuple_, dtype=np.object)}}# 使用train_no作为键,创建一个形状为tuple_的全零数组作为值。result = {**result, **{train_no: np.zeros(tuple_, dtype=np.object)}}# 使用train_no作为键,创建一个形状为tup
reading in sources list data from /etc/ros/rosdep/sources.list.dERROR: unable to process source [https://raw.githubusercontent.com/ros/rosdistro/master/rosdep/osx-homebrew.yaml]:<urlopen error [Err
D:\programfiles\miniconda\envs\py38torch_gpu\python.exe C:/Users/liqiang/Desktop/handpose_x-master/onnx_inference.pyTraceback (most recent call last):File "C:/Users/liqiang/Desktop/handpose_x-master/o
第一步:安装realsense SDK1.用源码进行安装:https://github.com/IntelRealSense/librealsense/然后将下载的源码安装包放在文件夹下面,我把文件夹放在了downloads下面:cd librealsense2.安装依赖;sudo apt-get install libudev-dev pkg-config libgtk-3-devsudo ap
File "C:/Users/liqiang/Desktop/scan/test.py", line 9, in <module>import pytesseractModuleNotFoundError: No module named 'pytesseract'Process finished with exit code 1在miniconda或者Anaconda环境里安装pyt