darknet

3 月，跳不动了？>>> 1.Yolov3的网络结构 想要转化为Caffe框架，就要先了解yolov3的网络结构，如下图。 如果有运行过darknet应该会很熟悉，这是darknet运行成功后打印log信息，这里面包含了yolo网络结构的一些信息。yolov3与v2相比，网络结构中加入了残差（shortcut层），并且引入了上采样（upsample层），并为了将采样后的特征图进行融合引入了拼接（route层），最后融合的特征图以三个不同的大小13*13*75,26*26*75,52*52*75输入给yolo层最后得到目标的位置及分类信息，加上卷积层convolution，这些便是yolov3的网络基本构造。因此只要我们如果在Caffe中找到对应的层按照相应的进行构造就能够使用Caffe实现yolov3了。 卷积层不说，yolov3中的shortcut层可以用eltwise替代，route层可以用concat替代，而upsample层和yolo层则需要自己实现，并添加到Caffe中即可。upsample层主要完成了上采样的工作，这里不细说。本文主要讲一下yolo层如何实现，上图中的YOLO Detection即为yolo层的所在位置，接收三种不同大小的特征图，并完成对特征图的解析，得到物体的位置和类别信息。所以其实yolo层主要起到了解析特征并输出检测结果的作用

I would like to use the yolo architecture for object detection. Before training the network with my custom data, I followed these steps to train it on the Pascal VOC data: https://pjreddie.com/darknet/yolo/ The instructions are very clear. But after the final step ./darknet detector train cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23 darknet immediately stops training and announces that weights have been written to the backups/ directory. At first I thought that the pretraining was simply too good and that the stopping criteria would be reached at once. So I've used the ./darknet detect

I am trying to train custom object classifier in Darknet YOLO v2 https://pjreddie.com/darknet/yolo/ I gathered a dataset for images most of them are 6000 x 4000 px and some lower resolutions as well. Do I need to resize the images before training to be squared ? I found that the config uses: [net] batch=64 subdivisions=8 height=416 width=416 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 thats why I was wondering how to use it for different sizes of data sets. You don't have to resize it, because Darknet will do it instead of you! It means you really don't