YOLO安装与配置

This post will guide you through detecting objects with the YOLO system using a pre-trained model. If you don't already have Darknet installed, you should do that first. Or instead of reading all that just run:

git clone https://github.com/pjreddie/darknet
cd darknet
make

Easy!

You already have the config file for YOLO in the cfg/ subdirectory. You will have to download the pre-trained weight file here (258 MB). Or just run this:

wget https://pjreddie.com/media/files/yolo.weights

Then run the detector!

./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg

You will see some output like this:

layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32
    1 max          2 x 2 / 2   416 x 416 x  32   ->   208 x 208 x  32
    .......
   29 conv    425  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 425
   30 detection
Loading weights from yolo.weights...Done!
data/dog.jpg: Predicted in 0.016287 seconds.
car: 54%
bicycle: 51%
dog: 56%

Darknet prints out the objects it detected, its confidence, and how long it took to find them. We didn't compile Darknet with OpenCV so it can't display the detections directly. Instead, it saves them in predictions.png. You can open it to see the detected objects. Since we are using Darknet on the CPU it takes around 6-12 seconds per image. If we use the GPU version it would be much faster.(我们没有用opencv编译Darknet，所以不能直接显示检测结果。但是，结果会保存在文件夹里)

I've included some example images to try in case you need inspiration. Trydata/eagle.jpg, data/dog.jpg, data/person.jpg, or data/horses.jpg!

The detect command is shorthand for a more general version of the command. It is equivalent to the command:（detect命令是更通用版本命令的缩写。 它相当于命令：）

./darknet detector test cfg/coco.data cfg/yolo.cfg yolo.weights data/dog.jpg

You don't need to know this if all you want to do is run detection on one image but it's useful to know if you want to do other things like run on a webcam (which you will seelater on).   如果你只是想在一张图像上运行一下检测结果，就不需要了解这个了，但是如果你想做一些其他的事，那就需要知道上面这句话。

Multiple Images

Instead of supplying an image on the command line, you can leave it blank to try multiple images in a row. Instead you will see a prompt when the config and weights are done loading:（除了在命令行上提供图像，您可以将其留空，以便连续尝试多个图像。 相反，当配置和权重完成加载时，您将看到一个提示：）

./darknet detect cfg/yolo.cfg yolo.weights
layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32
    1 max          2 x 2 / 2   416 x 416 x  32   ->   208 x 208 x  32
    .......
   29 conv    425  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 425
   30 detection
Loading weights from yolo.weights ...Done!
Enter Image Path:

Enter an image path like data/horses.jpg to have it predict boxes for that image.

Once it is done it will prompt you for more paths to try different images. Use Ctrl-C to exit the program once you are done.（一旦完成，它将提示您有更多路径尝试不同的图像。 完成后，使用Ctrl-C退出程序。）

Changing The Detection Threshold

By default, YOLO only displays objects detected with a confidence of .25 or higher. You can change this by passing the -thresh <val> flag to the yolo command. For example, to display all detection you can set the threshold to 0:（默认情况下，YOLO仅显示以0.25以上的置信度检测到的对象。 您可以通过将-thresh <val>标志传递给yolo命令来进行更改。 例如，要显示所有检测，您可以将阈值设置为0：）

./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg -thresh 0

Which produces:

So that's obviously not super useful but you can set it to different values to control what gets thresholded by the model.