下载模型及权重文件
模型下载地址：https://github.com/pjreddie/darknet 权重下载地址：https://pjreddie.com/darknet/yolo/
安装
参考https://blog.csdn.net/uncle_ll/article/details/80830112
CPU版本
git clone https://github.com/pjreddie/darknet.git
cd darknet
make这里如果自己的电脑支持Openmp的话,也可以更改Makefile文件将其中的OPENMP的值更改为1，会加快训练和测试速度
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0 # 若电脑支持Openmp时，可以将其设置为1
DEBUG=0ARCH= -gencode arch=compute_30,code=sm_30
-gencode arch=compute_35,code=sm_35
-gencode arch=compute_50,code=[sm_50,compute_50]
-gencode arch=compute_52,code=[sm_52,compute_52] \
-gencode arch=compute_60,code=[sm_60,compute_60]
# 这个地方是根据自己的GPU架构进行设置，不同架构的GPU的运算能力不一样，本文使用的是帕斯卡结构，查阅英伟达官网查看对应的计算能力为6.0
# -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated? # This is what I use, uncomment if you know your arch and want to specifyARCH= -gencode arch=compute_52,code=compute_52
VPATH=./src/:./examples
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/
CC=gcc
NVCC=nvcc # 这个地方若没有定义为环境变量，最好是使用绝对路径，大概位于/usr/local/cuda/bin/nvcc
数据形式介绍
1、数据分为图片和标注。二者为一一对应关系
2、标注可用LabelImg等标注软件生成
数据导入及配套文件生成
1.在darknet目录下创建一个新文件夹
2.在该文件夹下创建两个文件夹分别存放图片和xml标注文件
3.生成train.txt、val.txt、test.txt以及将标注文件转成txt格式
我的脚本：https://github.com/yuace/yolo_python 4.记录类别名称的.names文件
修改配置文件
1.修改cfg文件夹中的.data文件
2.修改cfg文件夹中的.cfg文件
在[net]层修改batch大小及调整迭代步长；
修改[yolo]层的classes，并将[yolo]层上一层的[convolutional]层的filters值按照如下公式修改：
filters = (classes + 5)*3
修改anchors，改为我们聚类的9个中心点
我的聚类脚本：https://github.com/yuace/yolo_python/blob/master/kmeans.py
注意：如果classes = 20，则filters = 75。注意网络中一共有3个yolo层，因此上述步骤一共要修改3次
训练、验证、测试
1.训练：
./darknet detector train cfg/xxx.data cfg/yolov3.cfg pretrained.weights我们通常使用预训练权重来训练：
wget https://pjreddie.com/media/files/darknet53.conv.74//下载预训练
./darknet detector train cfg/xxx.data cfg/yolov3.cfg darknet53.conv.74多gpu训练：
./darknet detector train cfg/xxx.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0,1,2,3如果中断，想继续训练，只需将预训练权重换成我们保存的权重即可。
2.验证：
./darknet detector recall cfg/xxx.data cfg/yolov3.cfg trained.weights3.测试：
./darknet detector test cfg/xxx.data cfg/yolov3.cfg trained.weights xxx.jpg
（注意：验证和测试时，将.cfg文件中的batch和subdivisions均改为1）
批量测试图片并保存在自定义文件夹下
参考：
1.用下面代码替换detector.c文件（example文件夹下）的void test_detector函数（注意有3处要改成自己的路径）
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
image **alphabet = load_alphabet();
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
double time;
char buff[256];
char *input = buff;
float nms=.45;
int i=0;
while(1){
if(filename){
strncpy(input, filename, 256);
image im = load_image_color(input,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile)
{
save_image(im, outfile);
}
else{
save_image(im, "predictions");#ifdef OPENCV
cvNamedWindow(“predictions”, CV_WINDOW_NORMAL);
if(fullscreen){
cvSetWindowProperty(“predictions”, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
show_image(im, “predictions”);
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}
free_image(im);
free_image(sized);
if (filename) break;
}
else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, “\n”);list *plist = get_paths(input);
char **paths = (char **)list_to_array(plist);
printf("Start Testing!\n");
int m = plist->size;
if(access("/home/xxxl/darknet/data/out",0)==-1)//"/home/xxx/darknet/data"修改成自己的路径
{
if (mkdir("/home/xxx/darknet/data/out",0777))//"/home/xxx/darknet/data"修改成自己的路径
{
printf("creat file bag failed!!!");
}
}
for(i = 0; i < m; ++i){
char *path = paths[i];
image im = load_image_color(path,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("Try Very Hard:");
printf("%s: Predicted in %f seconds.\n", path, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile){
save_image(im, outfile);
}
else{
char b[2048];
sprintf(b,"/home/xxx/darknet/data/out/%s",GetFilename(path));//"/home/xxx/darknet/data"修改成自己的路径
save_image(im, b);
printf("save %s successfully!\n",GetFilename(path));list *plist = get_paths(input);
char **paths = (char **)list_to_array(plist);
printf("Start Testing!\n");
int m = plist->size;
if(access("/home/xxxl/darknet/data/out",0)==-1)//"/home/xxx/darknet/data"修改成自己的路径
{
if (mkdir("/home/xxx/darknet/data/out",0777))//"/home/xxx/darknet/data"修改成自己的路径
{
printf("creat file bag failed!!!");
}
}
for(i = 0; i < m; ++i){
char *path = paths[i];
image im = load_image_color(path,0,0);
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
float *X = sized.data;
time=what_time_is_it_now();
network_predict(net, X);
printf("Try Very Hard:");
printf("%s: Predicted in %f seconds.\n", path, what_time_is_it_now()-time);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
//printf("%d\n", nboxes);
//if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
if(outfile){
save_image(im, outfile);
}
else{
char b[2048];
sprintf(b,"/home/xxx/darknet/data/out/%s",GetFilename(path));//"/home/xxx/darknet/data"修改成自己的路径
save_image(im, b);
printf("save %s successfully!\n",GetFilename(path));#ifdef OPENCV
cvNamedWindow(“predictions”, CV_WINDOW_NORMAL);
if(fullscreen){
cvSetWindowProperty(“predictions”, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
show_image(im, “predictions”);
cvWaitKey(0);
cvDestroyAllWindows();
#endif
}free_image(im);
free_image(sized);
if (filename) break;
}
}
}char *GetFilename(char *p)
{
static char name[20]={""};
char *q = strrchr(p,’/’) + 1;
strncpy(name,q,6);//注意后面的6，如果你的测试集的图片的名字字符（不包括后缀）是其他长度，请改为你需要的长度（官方的默认的长度是6）
return name;
}
3.在darknet下重新make
make clean
make4.执行批量测试命令会出现如下
Loading weights from yolov3.weights...Done!
Enter Image Path:Enter Image Path：后面输入你的txt文件路径（你准备好的所有测试图片的路径全部存放在一个txt文件里），如下
/home/xxx/darknet/data/test.txt之后就可以去自己的定义的out文件夹看结果了。
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