yolo算法的坐标输出代表什么? [英] What does the coordinate output of yolo algorithm represent?

问题描述

我的问题类似于本主题.当我开始考虑yolo算法的输出时，我正在观看Andrew Ng关于边界框预测的演讲.让我们考虑这个示例，我们使用19x19网格和只有一个2类的接收场，因此我们的输出将为=> 19x19x1x5.最后一个维度(大小为5的数组)表示以下内容:

  1)类(0或1)2)X坐标3)Y坐标4)边框的高度5)边框的宽度 

我不知道X，Y坐标是代表整个图像的大小还是正好接受域(过滤器)的边界框.在视频中，边界框被表示为接受域的一部分，但逻辑上的接受域比边界框小得多，而且人们可能会修改过滤器的大小，因此将边界框相对于过滤器放置是没有意义的.

那么，图像边界框的坐标基本上代表什么?

解决方案

来自

My question is similar to this topic. I was watching this lecture on bounding box prediction by Andrew Ng when I started thinking about output of yolo algorithm. Let's consider this example, We use 19x19 grids and only one receptive field with 2 classes, so our output will be => 19x19x1x5. The last dimension(array of size 5) represents the following:

1) The class (0 or 1)  
2) X-coordinate  
3) Y-coordinate  
4) height of the bounding box  
5) Width of the bounding box

I don't understand whether X,Y coordinates represent the bounding box with respect to the size of entire image or just and receptive field(filter). In the video the bounding box is represented as a part of receptive field but logically receptive field is much smaller than bounding box and also people might tinker with filter size, so positioning bounding boxes with respect to filter makes no sense.

So, basically what does the coordinates of bounding boxes of an image represent ?

解决方案

From Understanding YOLO post @ Hacker Noon:

Each grid cell predicts B bounding boxes as well as C class probabilities. The bounding box prediction has 5 components: (x, y, w, h, confidence). The (x, y) coordinates represent the center of the box, relative to the grid cell location (remember that, if the center of the box does not fall inside the grid cell, than this cell is not responsible for it). These coordinates are normalized to fall between 0 and 1. The (w, h) box dimensions are also normalized to [0, 1], relative to the image size. Let’s look at an example:

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