YOLO-V3实战（darknet）

一. 准备工作

1）实验环境：

　　darknet 是由 C 和 CUDA 开发的，不需要配置其他深度学习的框架（如，tensorflow、caffe 等），支持 CPU 和 GPU 运算，而且安装过程非常简单。本文使用的 CUDA 的版本如下所示：

　　CUDA：9.0
　　CUDNN：7.0
2）下载 github 源码：

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

3）配置darknet编译环境：

Ⅰ. 编译一直在darknet文件夹下；

Ⅱ. 每次修改 Makefile 文件后需重新 make 一下才能生效；

Ⅲ. 默认的 Makefile 是使用 CPU；

　　修改Makefile编译环境配置文件：

GPU=1 　　　# 是否打开GPU
CUDNN=1 　　# 是否打开cudnn
OPENCV=0　　# 是否打开opencv
OPENMP=0
DEBUG=1　　 # 是否进行debug

ARCH= -gencode arch=compute_61,code=compute_61　　# 根据GPU计算能力选择对应数值（GTX 1080Ti:61、Tesla K80:37）官网查看：https://developer.nvidia.com/cuda-gpus
...
NVCC=/usr/local/cuda-9.0/bin/nvcc 　　#修改nvcc路径
...
ifeq ($(GPU), 1)　　　　　　　#修改cuda路径--黄色部分，若不需要更改删除黄色部分即可
COMMON+= -DGPU -I/usr/local/cuda-9.0/include
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda-9.0/lib64 -lcuda -lcudart -lcublas -lcurand
endif

ifeq ($(CUDNN), 1)　　　　　　#修改cudnn路径--黄色部分，若不需要更改删除黄色部分即可
COMMON+= -DCUDNN -I/usr/local/cuda-9.0/include
CFLAGS+= -DCUDNN
LDFLAGS+= -L/usr/local/cuda-9.0/lib64 -lcudnn
endif

　　重新编译

make clean
make

　　执行./darknet就可以执行编译

4) 实验环境测试

　　下载预训练模型权重yolov3.weights

　　下载地址：https://pjreddie.com/media/files/yolov3.weights

　　基于yolov3.weights模型权重的测试

测试单张图片(下面两个指令相同)
./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/dog.jpg
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
测试多张图片
./darknet detect cfg/yolov3.cfg yolov3.weights
~根据提示输入图片路径

　　输出：保存./darknet目录下的predict.png

5) ./darknet编译格式

./darknet detector test <data_cfg> <models_cfg> <weights> <test_file> [-thresh] [-out]
./darknet detector train <data_cfg> <models_cfg> <weights> [-thresh] [-gpu] [-gpus] [-clear]
./darknet detector valid <data_cfg> <models_cfg> <weights> [-out] [-thresh]
./darknet detector recall <data_cfg> <models_cfg> <weights> [-thresh]

'<>'必选项，’[ ]‘可选项

　　data_cfg：数据配置文件，eg：cfg/voc.data

　　models_cfg：模型配置文件，eg：cfg/yolov3-voc.cfg

　　weights：权重配置文件，eg：weights/yolov3.weights

　　test_file：测试文件，eg：*/*/*/test.txt

　　-thresh：显示被检测物体中confidence大于等于 [-thresh] 的bounding-box，默认0.005

　　-out：输出文件名称，默认路径为results文件夹下，eg：-out "" //输出class_num个文件，文件名为class_name.txt；若不选择此选项，则默认输出文件名为comp4_det_test_"class_name".txt

　　-i/-gpu：指定单个gpu，默认为0，eg：-gpu 2

　　-gpus：指定多个gpu，默认为0，eg：-gpus 0,1,2

二. 实验步骤

基于darknet在VOC格式的数据集上训练yolov3

1）下载 Pascal VOC 2007 和 2012 的数据集

wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar

　　注：若非voc格式的数据集需先生成voc格式的数据集，数据集的目录严格按照voc数据集的目录结构

2）生成 VOC 数据集的标签

　　darknet 需要一个“.txt”格式的标签文件，每行表示一张图像的信息，包括（x, y, w, h）格式为：<object-class> <x> <y> <width> <height>

　　darknet 官网提供了一个针对 VOC 数据集，处理标签的脚本，darknet/scripts文件夹下的voc_label.py文件，若无执行如下命令下载文件：

wget https://pjreddie.com/media/files/voc_label.py # 获取脚本

　　修改 voc_label.py（4处）

①sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] #替换为自己的数据集
②classes = ["head", "eye", "nose"] #修改为自己的类别
③in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) #将数据集放于当前目录下
④os.system("cat 2007_train.txt 2007_val.txt > train.txt") #修改为自己的数据集用作训练

　　修改完成后执行：

python voc_label.py　　　　# 执行脚本，获得所需的“.txt”文件

　　生成的xml文件在 VOCdevkit/VOC%s/labels 目录下

　　标签处理完成后，在voc_label.py所在目录下生成如下几个文件：

　　2007_text.txt、2007_train.txt、2007_val.txt、2012_text.txt、2012_train.txt、train.txt、train_all.txt

3）修改配置文件

　　Ⅰ. 数据配置文件——cfg/voc.data

classes= 20　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　             # 类别总数
train = /home/xieqi/project/train.txt 　　　　  　　　　　　　　　　　　　　　　 # 训练数据所在的位置
valid = /home/xieqi/project/2007_test.txt　　　 　　　　　　　　　　　　　　　　 # 测试数据所在的位置
names = data/voc.names　　　　　　　　　　　　　　　　　　　　　　　　 　　　　　　  # 修改见voc.names
backup = backup 　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 # 输出的权重信息保存的文件夹
eval = 
results =

　　Ⅱ. 模型配置文件——cfg/yolov3-voc.cfg

batch=64　　　　　　   # 一批训练样本的样本数量，每batch个样本更新一次参数
subdivisions=32　　   # 它会让你的每一个batch不是一下子都丢到网络里。而是分成subdivision对应数字的份数，一份一份的跑完后，在一起打包算作完成一次iteration
width=416　　　　     # 只可以设置成32的倍数
height=416　　　　    # 只可以设置成32的倍数

channels=3　　　　　　 # 若为灰度图，则chennels=1，另外还需修改/scr/data.c文件中的load_data_detection函数；若为RGB则 channels=3 ，无需修改/scr/data.c文件

momentum=0.9　　      # 最优化方法的动量参数，这个值影响着梯度下降到最优值得速度 
decay=0.0005　　   　 # 权重衰减正则项，防止过拟合
angle=0　　　　    　　# 通过旋转角度来生成更多训练样本
saturation = 1.5　　  # 通过调整饱和度来生成更多训练样本
exposure = 1.5　　    # 通过调整曝光量来生成更多训练样本
hue=.1　　　　　     　# 通过调整色调来生成更多训练样本


learning_rate=0.001        # 学习率, 刚开始训练时, 以 0.01 ~ 0.001 为宜, 一定轮数过后,逐渐减缓。
burn_in=1000　　　      　　 # 在迭代次数小于burn_in时，其学习率的更新有一种方式，大于burn_in时，才采用policy的更新方式
max_batches = 50200  　　   # 训练步数
policy=steps　　　　　 　　   # 学习率调整的策略
steps=40000,45000     　    # 开始衰减的步数
scales=.1,.1　　　　 　　   　# 在第40000和第45000次迭代时，学习率衰减10倍
...
[convolutional]——YOLO层前一层卷积层
...
filters=24 　　　　　　 　　  # 每一个[yolo]层前的最后一个卷积层中的 filters=num(yolo层个数)*(classes+5)
...

[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326　　#如果想修改默认anchors数值，使用k-means即可；
classes=3  　　　　　　# 修改为自己的类别数
num=9　　　　　　　   　# 每个grid cell预测几个box,和anchors的数量一致。调大num后训练时Obj趋近0的话可以尝试调大object_scale
jitter=.3　　　　　　　# 利用数据抖动产生更多数据, jitter是crop的参数, jitter=.3，就是在0~0.3中进行crop
ignore_thresh = .5   # 决定是否需要计算IOU误差的参数，大于thresh，IOU误差不会夹在cost function中
truth_thresh = 1
random=1　　　　　　　  # 如果为1，每次迭代图片大小随机从320到608，步长为32，如果为0，每次训练大小与输入大小一致
...

　　Ⅲ. 标签配置文件——data/voc.names

head #自己需要探测的类别，一行一个
eye
nose

　　 Ⅳ. 修改数据格式——scr/data.c

data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure)
{
    char **random_paths = get_random_paths(paths, n, m);
    int i;
    data d = {0};
    d.shallow = 0;

    d.X.rows = n;
    d.X.vals = calloc(d.X.rows, sizeof(float*));
    d.X.cols = h*w;                   //灰阶图
    //d.X.cols = h*w*3;               //RGB图
    
　　...

4）下载预训练的参数（卷积权重）

　　“darknet53.conv.74”是使用 Imagenet 数据集进行预训练：

wget https://pjreddie.com/media/files/darknet53.conv.74 # 下载预训练的网络模型参数

　　获取其他模型类似 darknet53.conv.74 的预训练权重

　　　　Ⅰ. 下载官方预训练模型的权重

　　　　　　https://pjreddie.com/darknet/yolo/　　　　//COCO数据集训练的

　　　　　　https://pjreddie.com/darknet/imagenet/　　//Imagenet数据集训练的

　　　　　eg： wget https://pjreddie.com/media/files/yolov3-tiny.weights

　　　　Ⅱ. 转换为类似darknet.conv.74的预训练权重

　　　　　　https://github.com/AlexeyAB/darknet/blob/57e878b4f9512cf9995ff6b5cd6e0d7dc1da9eaf/build/darknet/x64/partial.cmd#L24

　　　　　eg： ./darknet partial cfg/yolov3-tiny yolov3-tiny.weights yolov-tiny.conv.15 15

5）训练模型：

　　Ⅰ. 单GPU训练: ./darknet detector train <data_cfg> <train_cfg> <weights> -gpu <gpu_id>

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -i 1

　　Ⅱ. 多GPU训练: ./darknet detector train <data_cfg> <model_cfg> <weights> -gpus <gpu_list>

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gpus 0,1,2,3

　　Ⅲ. 从checkpoint继续训练

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc.backup -gpus 0,1,2,3

　　Ⅳ. CPU训练:

./darknet detector train <data_cfg> <model_cfg> <weights> -nogpu

　　Ⅴ. 生成loss-iter曲线

　　在执行训练命令的时候加一下管道，tee一下log：

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg | tee result/log/training.log

　　将下面的python代码保存为drawcurve.py。并执行

python  drawcurve.py training.log 0

　　~drawcurve.py

import argparse
import sys
import matplotlib.pyplot as plt
def main(argv):
    parser = argparse.ArgumentParser()
    parser.add_argument("log_file",  help = "path to log file"  )
    parser.add_argument( "option", help = "0 -> loss vs iter"  )
    args = parser.parse_args()
    f = open(args.log_file)
    lines  = [line.rstrip("\n") for line in f.readlines()]
    # skip the first 3 lines
    lines = lines[3:]
    numbers = {'1','2','3','4','5','6','7','8','9','0'}
    iters = []
    loss = []
    for line in lines:
        if line[0] in numbers:
            args = line.split(" ")
            if len(args) >3:
                iters.append(int(args[0][:-1]))
                loss.append(float(args[2]))
    plt.plot(iters,loss)
    plt.xlabel('iters')
    plt.ylabel('loss')
    plt.grid()
    plt.show()
if __name__ == "__main__":
    main(sys.argv)

6）训练过程：

　　训练的log格式如下：

Loaded: 4.533954 seconds
Region Avg IOU: 0.262313, Class: 1.000000, Obj: 0.542580, No Obj: 0.514735, Avg Recall: 0.162162,  count: 37
Region Avg IOU: 0.175988, Class: 1.000000, Obj: 0.499655, No Obj: 0.517558, Avg Recall: 0.070423,  count: 71
Region Avg IOU: 0.200012, Class: 1.000000, Obj: 0.483404, No Obj: 0.514622, Avg Recall: 0.075758,  count: 66
Region Avg IOU: 0.279284, Class: 1.000000, Obj: 0.447059, No Obj: 0.515849, Avg Recall: 0.134615,  count: 52
1: 629.763611, 629.763611 avg, 0.001000 rate, 6.098687 seconds, 64 images
Loaded: 2.957771 seconds
Region Avg IOU: 0.145857, Class: 1.000000, Obj: 0.051285, No Obj: 0.031538, Avg Recall: 0.069767,  count: 43
Region Avg IOU: 0.257284, Class: 1.000000, Obj: 0.048616, No Obj: 0.027511, Avg Recall: 0.078947,  count: 38
Region Avg IOU: 0.174994, Class: 1.000000, Obj: 0.030197, No Obj: 0.029943, Avg Recall: 0.088889,  count: 45
Region Avg IOU: 0.196278, Class: 1.000000, Obj: 0.076030, No Obj: 0.030472, Avg Recall: 0.087719,  count: 57
2: 84.804230, 575.267700 avg, 0.001000 rate, 5.959159 seconds, 128 images
iter  总损失      平均损失           学习率           花费时间    参与训练的图片总数

Region	cfg文件中yolo-layer的索引
Avg IOU	当前迭代中，预测的box与标注的box的平均交并比，越大越好，期望数值为1
Class	标注物体的分类准确率，越大越好，期望数值为1
obj	越大越好，期望数值为1
No obj	越小越好，但不为零
.5R	以IOU=0.5为阈值时候的recall; recall = 检出的正样本/实际的正样本
.75R	以IOU=0.75为阈值时候的recall
count	正样本数目

Region 82 Avg IOU: 0.798032, Class: 0.559781, Obj: 0.515851, No Obj: 0.006533, .5R: 1.000000, .75R: 1.000000, count: 2
Region 94 Avg IOU: 0.725307, Class: 0.830518, Obj: 0.506567, No Obj: 0.000680, .5R: 1.000000, .75R: 0.750000, count: 4
Region 106 Avg IOU: 0.579333, Class: 0.322556, Obj: 0.020537, No Obj: 0.000070, .5R: 1.000000, .75R: 0.000000, count: 2

　　以上输出显示了所有训练图片的一个批次（batch），批次大小的划分根据我们在 .cfg 文件中设置的subdivisions参数。

　　在我使用的 .cfg 文件中 batch = 64 ，subdivision = 16，所以在训练输出中，训练迭代包含了16组，每组又包含了4张图片，跟设定的batch和subdivision的值一致。

　　但是此处有16*3条信息，每组包含三条信息，分别是：Region 82、Region 94、Region 106。

　　三个尺度上预测不同大小的框：

　　　　82卷积层为最大的预测尺度，使用较大的mask，但是可以预测出较小的物体；

　　　　94卷积层为中间的预测尺度，使用中等的mask；

　　　　106卷积层为最小的预测尺度，使用较小的mask，可以预测出较大的物体

　　每个batch都会有这样一个输出：

2706: 1.350835, 1.386559 avg, 0.001000 rate, 3.323842 seconds, 173184 images
batch   总损失      平均损失       学习率　　　　　  　花费时间　     参与训练的图片总数 = 2706 * 64

三. 模型评价

1）更改配置文件：

　　Ⅰ. 模型参数(cfg/yoloc3.cfg)：

　　　　batch=1

　　　　subdivisions=1

注：测评模型时batch、subdivisions必须为1

　　Ⅱ. 更改为批处理(example/detector.c)：

　　　　①. 在detector.c中增加头文件：

#include <unistd.h>  /* Many POSIX functions (but not all, by a large margin) */
#include <fcntl.h>   /* open(), creat() - and fcntl() */

　　　　②. 在前面添加GetFilename(char *fullname)函数

#include <unistd.h>
#include <fcntl.h>

#include "darknet.h"
#include <sys/stat.h>
#include <stdio.h>
#include <time.h>
#include <sys/types.h>

static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,
　　　　　　　　　　　　　　 36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,
　　　　　　　　　　 　　　　67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};

//产生的文件名与原文件名相同（去除路径和格式）
char *GetFilename(char *fullname)
{
    int from,to,i;
    char *newstr,*temp;
    if(fullname!=NULL){
        //if not find dot
        if((temp=strchr(fullname,'.'))==NULL){
        newstr = fullname;
        }
        else
        {
            from = strlen(fullname) - strlen(temp);
            to = (temp-fullname);
            //the first dot's index
            for (i=from; i<=to; i--){
                if (fullname[i]=='.') break;//find the last dot
            }
            newstr = (char*)malloc(i+1);
            strncpy(newstr,fullname,i);
            *(newstr+i)=0;
        }
    }
    static char name[50] = {""};
    char *q = strrchr(newstr,'/') + 1;
    strncpy(name,q,40);
    return name;
}

　　　　③. 用下面代码替换detector.c文件的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/xieqi/darknet/data/out_img",0)==-1)　　//修改成自己的路径
            {
              if (mkdir("/home/xieqi/darknet/data/out_img",0777))　　//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/xieqi/darknet/data/out_img/%s",GetFilename(path));　　//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;
        }
      }
    }
}

　　　　④. `validate_detector_recall`函数定义和调用改为：

void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
validate_detector_recall(datacfg, cfg, weights);

　　　　`⑤.validate_detector_recall`内的`plist`和`paths`的如下初始化代码：

list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);

　　　　修改为：

list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.list");
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);

　Ⅲ. 在darknet下重新make

make clean
make

2). 测试单张图片

　　Ⅰ. ./darknet detector test <data_cfg> <models_cfg> <weights> <image_path> 　　# 本次测试无opencv支持

　　Ⅱ. <models_cfg>文件中batch和subdivisions两项必须为1；

　　Ⅲ. 测试时还可以用-thresh和-hier选项指定对应参数；

　　Ⅳ. 结果都保存在./data/out_img 文件夹下

./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_final.weights Eminem.jpg　　　　    # 测试单张图片

3). 生成预测结果

　　Ⅰ. 批量测试——输出文本检测结果

./darknet detector valid cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_final.weights -i 1 -out ""

　　　　① ./darknet detector valid <data_cfg> <models_cfg> <weights>；

　　　　② 结果生成在<data_cfg>的指定的目录下以<out_file>开头的若干文件中，若<data_cfg>没有指定results，那么默认为<darknet_root>/results；
　　　　③ <models_cfg>文件中batch和subdivisions两项必须为1；

　　　　④ 若-out 未指定字符串，则在results文件夹下生成comp4_det_test_[类名].txt文件并保存测试结果；

　　　　⑤ 本次实验在results文件夹下生成 [类名].txt 文件；

　　Ⅱ. 批量测试——输出图片检测结果

./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_final.weights -i 2 #enter

Enter Image Path: data/voc/2007_test.txt

　　　　① ./darknet detector test <data_cfg> <models_cfg> <weights>；

　　　　② <models_cfg>文件中batch和subdivisions两项必须为1；
　　　　③ 本次实验结果，在<darknet_root>/data/out_img文件夹下（detector.c/test_detector函数更改的路径），文件名以原图片名前6位命名（detector.c/GetFilename函数定义的位数）；

4). 计算recall（执行这个命令需要修改detector.c文件，修改信息请参考“detector.c修改”）

　　Ⅰ. ./darknet detector recall <data_cfg> <test_cfg> <weights>；
　　Ⅱ. <test_cfg>文件中batch和subdivisions两项必须为1；
　　Ⅲ. 输出在stderr里，重定向时请注意；
　　Ⅳ. RPs/Img、IOU、Recall都是到当前测试图片的均值；
　　Ⅴ. detector.c中对目录处理有错误，可以参照validate_detector对validate_detector_recall最开始几行的处理进行修改；

./darknet detector valid cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_20000.weights

　　最后得到的log如下：

306   746   783       RPs/Img: 21.45  IOU: 75.59%     Recall:95.27%
307   748   785       RPs/Img: 21.43  IOU: 75.62%     Recall:95.29%
308   750   787       RPs/Img: 21.42  IOU: 75.59%     Recall:95.30%
309   752   789       RPs/Img: 21.43  IOU: 75.62%     Recall:95.31%
310   754   791       RPs/Img: 21.44  IOU: 75.63%     Recall:95.32%
No.  Correct total

　　输出的具体格式为：

Number	处理的第几张图
Correct	正确的识别出了多少bbox。这个值算出来的步骤是这样的，丢进网络一张图片，网络会预测出很多bbox，每个bbox都有其置信概率，概率大于threshold的bbox与实际的bbox，也就是labels中txt的内容计算IOU，找出IOU最大的bbox，如果这个最大值大于预设的IOU的threshold，那么correct加一
Total	实际有多少个bbox
Rps/img	平均每个图片会预测出来多少个bbox
IOU	预测出的bbox和实际标注的bbox的交集除以他们的并集。显然，这个数值越大，说明预测的结果越好
Recall	召回率，检测出物体的个数除以标注的所有物体个数。通过代码我们也能看出来就是Correct除以Total的值

　　error：

　　本次实验出现 IOU：inf% —— 交并比数值爆炸

　　打印bbox详细信息(修改detector.c/validate_detector_recall)

        for (j = 0; j < num_labels; ++j) {
            ++total;
            box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
            printf("truth x=%f, y=%f, w=%f,h=%f\n",truth[j].x,truth[j].y,truth[j].w,truth[j].h);//添加代码
            float best_iou = 0;
            for(k = 0; k < l.w*l.h*l.n; ++k){
                float iou = box_iou(dets[k].bbox, t);
                if(dets[k].objectness > thresh && iou > best_iou){
                    printf("predict=%f x=%f, y=%f, w=%f,h=%f\n",dets[k].objectness,dets[k].bbox.x,dets[k].bbox.y,dets[k].bbox.w,dets[k].bbox.h);//添加代码
                    best_iou = iou;
                }
            }

　　由显示结果可以看出，预测框的中心坐标x和y出现-nan

　　解决方法：修改函数

void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
    ...
    //layer l = net->layers[net->n-1]; //注释掉
    ...
            for(k = 0; k < l.w*l.h*l.n; ++k){ //改为for(k = 0; k < nboxes; ++k){
    ...

5). 计算AP、mAP

先使用 3)-Ⅰ计算出验证集结果，再使用py-faster-rcnn下的voc_eval.py计算AP、mAP

　　Ⅰ. 下载voc_eval.py到 darknet 的根目录

　　　　voc_eval.py下载地址：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py

　　　　根据标签修改voc_eval.py的内容

.xml文件结构中

      ①若无pose
22    #obj_struct['pose'] = obj.find('pose').text 　　　　　　　　　　     　　　　　　　　　　#    注释掉

      ②若无truncated
23    #obj_struct['truncated'] = int(obj.find('truncated').text) 　　　　　　　　　　　　　　#    注释掉

      ③若无difficult
24    #obj_struct['difficult'] = int(obj.find('difficult').text)    　　　　　　　　　　　　#    注释掉
137    #if use_diff:　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 #    注释掉
138        #difficult = np.array([False for x in R]).astype(np.bool)　　　　　　　　　　　  #　　注释掉
139    #else:　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　#    注释掉　　　　　　　　　　　
140        #difficult = np.array([x['difficult'] for x in R]).astype(np.bool)　　　　　　 #　　 注释掉
142    npos = npos +len(R)    　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　   #　　 更改npos = npos + sum(~difficult)
145    #'difficult': difficult,    　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 #    注释掉
197    #if not R['difficult'][jmax]:    　　　　　　　　　　　　　　　　　　　　　　　　　　　　　#　　注释掉

　　Ⅱ. 计算单类AP

　　　　①. 在darknet根目录下新建computer_mAP.py

#!/home/xieqi/anaconda2/envs/py2.7/bin python2.7
# -*- coding:utf-8 -*-

from voc_eval import voc_eval

print(voc_eval('/home/xieqi/darknet/results/{}.txt', '/home/xieqi/project/traffic_object_detection/voc_data/Annotations/{}.xml', 
'/home/xieqi/project/traffic_object_detection/voc_data/ImageSets/Main/test.txt', 'vehicle', '/home/xieqi/project/traffic_object_detection/result/'))

# "第一个参数为detector valid 按类别分类后的txt路径"
# "第二个参数为验证集对应的xml标签路径"
# "第三个为验证集txt文本路径，内容必须是无路径无后缀的图片名"
# "第四个为待验证的类别名"
# "第五个为pkl文件保存的路径"

　　　　②. 用python2执行computer_mAP.py

　　　　　　① 重复执行，检测其他类别需要删除生成的annots.pkl文件或改变computer_mAP.py中pkl文件保存的路径

　　　　　　② 输出两个array()，分别为rec和prec，最后一个数字为单类AP。

　　Ⅲ. 计算总mAP

　　　　在darknet根目录下新建computer_all_mAP.py

#!/home/xieqi/anaconda2/envs/py2.7/bin python2.7
# -*- coding:utf-8 -*-

from voc_eval import voc_eval

import os

current_path = os.getcwd()
results_path = current_path+"/results"
sub_files = os.listdir(results_path)

mAP = []
for i in range(len(sub_files)):
    class_name = sub_files[i].split(".txt")[0]
    rec, prec, ap = voc_eval('/home/xieqi/darknet/results/{}.txt',
　　　　　　　　　　　　　　　　　'/home/xieqi/project/traffic_object_detection/voc_data/Annotations/{}.xml',
　　　　　　　　　　　　　　　　　'/home/xieqi/project/traffic_object_detection/voc_data/ImageSets/Main/test.txt',
　　　　　　　　　　　　　　　　　class_name, '/home/xieqi/project/traffic_object_detection/result/mAP')
    print("{} :\t {} ".format(class_name, ap))
    mAP.append(ap)

mAP = tuple(mAP)

print("***************************")
print("mAP :\t {}".format( float( sum(mAP)/len(mAP)) ))

# results文件夹只能有'类名.txt'文件
# test.txt文件包含所有results中txt文件的图片名（保证是共同的验证集）
# 保证最后的输出路径下无pkl文件。
# 上述代码中的ap就是针对输入单一类别计算出的AP。

四、文件解析

1). ~detector.c

#include <unistd.h>
#include <fcntl.h>

#include "darknet.h"
#include <sys/stat.h>
#include <stdio.h>
#include <time.h>
#include <sys/types.h>

static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,
41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};

char *GetFilename(char *fullname)
{
    int from,to,i;
    char *newstr,*temp;
    if(fullname!=NULL){
        //if not find dot
        if((temp=strchr(fullname,'.'))==NULL){
        newstr = fullname;
        }
        else
        {
            from = strlen(fullname) - strlen(temp);
            to = (temp-fullname);
            //the first dot's index
            for (i=from; i<=to; i--){
                if (fullname[i]=='.') break;//find the last dot
            }
            newstr = (char*)malloc(i+1);
            strncpy(newstr,fullname,i);
            *(newstr+i)=0;
        }
    }
    static char name[50] = {""};
    char *q = strrchr(newstr,'/') + 1;
    strncpy(name,q,40);
    return name;
}

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
    list *options = read_data_cfg(datacfg); //解析data文件，用自定义链表options存储训练集基本信息，函数位于option_list.c
    char *train_images = option_find_str(options, "train", "data/train.list");  //从options中找训练集
    char *backup_directory = option_find_str(options, "backup", "/backup/");    //从options中找backup路径

    srand(time(0)); //初始化随机种子数
    char *base = basecfg(cfgfile);  //此函数位于utils.c,返回cfg文件不带后缀的名字
    printf("%s\n", base);
    float avg_loss = -1;
    network **nets = calloc(ngpus, sizeof(network));

    srand(time(0));
    int seed = rand();
    int i;
    for(i = 0; i < ngpus; ++i){
        srand(seed);
#ifdef GPU
        cuda_set_device(gpus[i]);
#endif
        nets[i] = load_network(cfgfile, weightfile, clear);
        nets[i]->learning_rate *= ngpus;
    }
    srand(time(0));
    network *net = nets[0];

    int imgs = net->batch * net->subdivisions * ngpus;
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
    data train, buffer;

    layer l = net->layers[net->n - 1];

    int classes = l.classes;
    float jitter = l.jitter;

    list *plist = get_paths(train_images);
    //int N = plist->size;
    char **paths = (char **)list_to_array(plist);

    load_args args = get_base_args(net);
    args.coords = l.coords;
    args.paths = paths;
    args.n = imgs;
    args.m = plist->size;
    args.classes = classes;
    args.jitter = jitter;
    args.num_boxes = l.max_boxes;
    args.d = &buffer;
    args.type = DETECTION_DATA;
    //args.type = INSTANCE_DATA;
    args.threads = 64;

    pthread_t load_thread = load_data(args);
    double time;
    int count = 0;
    //while(i*imgs < N*120){
    while(get_current_batch(net) < net->max_batches){
        if(l.random && count++%10 == 0){
            printf("Resizing\n");
            int dim = (rand() % 10 + 10) * 32;
            if (get_current_batch(net)+200 > net->max_batches) dim = 608;
            //int dim = (rand() % 4 + 16) * 32;
            printf("%d\n", dim);
            args.w = dim;
            args.h = dim;

            pthread_join(load_thread, 0);
            train = buffer;
            free_data(train);
            load_thread = load_data(args);

            #pragma omp parallel for
            for(i = 0; i < ngpus; ++i){
                resize_network(nets[i], dim, dim);
            }
            net = nets[0];
        }
        time=what_time_is_it_now();
        pthread_join(load_thread, 0);
        train = buffer;
        load_thread = load_data(args);

        /*
           int k;
           for(k = 0; k < l.max_boxes; ++k){
           box b = float_to_box(train.y.vals[10] + 1 + k*5);
           if(!b.x) break;
           printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
           }
         */
        /*
           int zz;
           for(zz = 0; zz < train.X.cols; ++zz){
           image im = float_to_image(net->w, net->h, 3, train.X.vals[zz]);
           int k;
           for(k = 0; k < l.max_boxes; ++k){
           box b = float_to_box(train.y.vals[zz] + k*5, 1);
           printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
           draw_bbox(im, b, 1, 1,0,0);
           }
           show_image(im, "truth11");
           cvWaitKey(0);
           save_image(im, "truth11");
           }
         */

        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);

        time=what_time_is_it_now();
        float loss = 0;
#ifdef GPU
        if(ngpus == 1){
            loss = train_network(net, train);
        } else {
            loss = train_networks(nets, ngpus, train, 4);
        }
#else
        loss = train_network(net, train);
#endif
        if (avg_loss < 0) avg_loss = loss;
        avg_loss = avg_loss*.9 + loss*.1;

        i = get_current_batch(net);
        printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs);
        if(i%100==0){
#ifdef GPU
            if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s.backup", backup_directory, base);
            save_weights(net, buff);
        }
        if(i%10000==0 || (i < 1000 && i%100 == 0)){
#ifdef GPU
            if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
            save_weights(net, buff);
        }
        free_data(train);
    }
#ifdef GPU
    if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
    char buff[256];
    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
    save_weights(net, buff);
}


static int get_coco_image_id(char *filename)
{
    char *p = strrchr(filename, '/');
    char *c = strrchr(filename, '_');
    if(c) p = c;
    return atoi(p+1);
}

static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
{
    int i, j;
    int image_id = get_coco_image_id(image_path);
    for(i = 0; i < num_boxes; ++i){
        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;

        if (xmin < 0) xmin = 0;
        if (ymin < 0) ymin = 0;
        if (xmax > w) xmax = w;
        if (ymax > h) ymax = h;

        float bx = xmin;
        float by = ymin;
        float bw = xmax - xmin;
        float bh = ymax - ymin;

        for(j = 0; j < classes; ++j){
            if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
        }
    }
}

void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
{
    int i, j;
    for(i = 0; i < total; ++i){
        float xmin = dets[i].bbox.x - dets[i].bbox.w/2. + 1;
        float xmax = dets[i].bbox.x + dets[i].bbox.w/2. + 1;
        float ymin = dets[i].bbox.y - dets[i].bbox.h/2. + 1;
        float ymax = dets[i].bbox.y + dets[i].bbox.h/2. + 1;

        if (xmin < 1) xmin = 1;
        if (ymin < 1) ymin = 1;
        if (xmax > w) xmax = w;
        if (ymax > h) ymax = h;

        for(j = 0; j < classes; ++j){
            if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
                    xmin, ymin, xmax, ymax);
        }
    }
}

void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
{
    int i, j;
    for(i = 0; i < total; ++i){
        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;

        if (xmin < 0) xmin = 0;
        if (ymin < 0) ymin = 0;
        if (xmax > w) xmax = w;
        if (ymax > h) ymax = h;

        for(j = 0; j < classes; ++j){
            int class = j;
            if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, dets[i].prob[class],
                    xmin, ymin, xmax, ymax);
        }
    }
}

void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{
    int j;
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "data/train.list");
    char *name_list = option_find_str(options, "names", "data/names.list");
    char *prefix = option_find_str(options, "results", "results");
    char **names = get_labels(name_list);
    char *mapf = option_find_str(options, "map", 0);
    int *map = 0;
    if (mapf) map = read_map(mapf);

    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 2);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
    srand(time(0));

    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

    layer l = net->layers[net->n-1];
    int classes = l.classes;

    char buff[1024];
    char *type = option_find_str(options, "eval", "voc");
    FILE *fp = 0;
    FILE **fps = 0;
    int coco = 0;
    int imagenet = 0;
    if(0==strcmp(type, "coco")){
        if(!outfile) outfile = "coco_results";
        snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
        fp = fopen(buff, "w");
        fprintf(fp, "[\n");
        coco = 1;
    } else if(0==strcmp(type, "imagenet")){
        if(!outfile) outfile = "imagenet-detection";
        snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
        fp = fopen(buff, "w");
        imagenet = 1;
        classes = 200;
    } else {
        if(!outfile) outfile = "comp4_det_test_";
        fps = calloc(classes, sizeof(FILE *));
        for(j = 0; j < classes; ++j){
            snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
            fps[j] = fopen(buff, "w");
        }
    }

    int m = plist->size;
    int i=0;
    int t;

    float thresh = .005;
    float nms = .45;

    int nthreads = 4;
    image *val = calloc(nthreads, sizeof(image));
    image *val_resized = calloc(nthreads, sizeof(image));
    image *buf = calloc(nthreads, sizeof(image));
    image *buf_resized = calloc(nthreads, sizeof(image));
    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));

    image input = make_image(net->w, net->h, net->c*2);

    load_args args = {0};
    args.w = net->w;
    args.h = net->h;
    //args.type = IMAGE_DATA;
    args.type = LETTERBOX_DATA;

    for(t = 0; t < nthreads; ++t){
        args.path = paths[i+t];
        args.im = &buf[t];
        args.resized = &buf_resized[t];
        thr[t] = load_data_in_thread(args);
    }
    double start = what_time_is_it_now();
    for(i = nthreads; i < m+nthreads; i += nthreads){
        fprintf(stderr, "%d\n", i);
        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
            pthread_join(thr[t], 0);
            val[t] = buf[t];
            val_resized[t] = buf_resized[t];
        }
        for(t = 0; t < nthreads && i+t < m; ++t){
            args.path = paths[i+t];
            args.im = &buf[t];
            args.resized = &buf_resized[t];
            thr[t] = load_data_in_thread(args);
        }
        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
            char *path = paths[i+t-nthreads];
            char *id = basecfg(path);
            copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data, 1);
            flip_image(val_resized[t]);
            copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data + net->w*net->h*net->c, 1);

            network_predict(net, input.data);
            int w = val[t].w;
            int h = val[t].h;
            int num = 0;
            detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &num);
            if (nms) do_nms_sort(dets, num, classes, nms);
            if (coco){
                print_cocos(fp, path, dets, num, classes, w, h);
            } else if (imagenet){
                print_imagenet_detections(fp, i+t-nthreads+1, dets, num, classes, w, h);
            } else {
                print_detector_detections(fps, id, dets, num, classes, w, h);
            }
            free_detections(dets, num);
            free(id);
            free_image(val[t]);
            free_image(val_resized[t]);
        }
    }
    for(j = 0; j < classes; ++j){
        if(fps) fclose(fps[j]);
    }
    if(coco){
        fseek(fp, -2, SEEK_CUR); 
        fprintf(fp, "\n]\n");
        fclose(fp);
    }
    fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
}


void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{
    int j;
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "/home/xieqi/project/traffic_object_detection/voc_data/test.txt");
    char *name_list = option_find_str(options, "names", "data/53w.names");
    char *prefix = option_find_str(options, "results", "results");
    char **names = get_labels(name_list);
    char *mapf = option_find_str(options, "map", 0);
    int *map = 0;
    if (mapf) map = read_map(mapf);

    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
    srand(time(0));

    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

    layer l = net->layers[net->n-1];
    int classes = l.classes;

    char buff[1024];
    char *type = option_find_str(options, "eval", "voc");
    FILE *fp = 0;
    FILE **fps = 0;
    int coco = 0;
    int imagenet = 0;
    if(0==strcmp(type, "coco")){
        if(!outfile) outfile = "coco_results";
        snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
        fp = fopen(buff, "w");
        fprintf(fp, "[\n");
        coco = 1;
    } else if(0==strcmp(type, "imagenet")){
        if(!outfile) outfile = "imagenet-detection";
        snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
        fp = fopen(buff, "w");
        imagenet = 1;
        classes = 200;
    } else {
        if(!outfile) outfile = "comp4_det_test_";
        fps = calloc(classes, sizeof(FILE *));
        for(j = 0; j < classes; ++j){
            snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
            fps[j] = fopen(buff, "w");
        }
    }


    int m = plist->size;
    int i=0;
    int t;

    float thresh = .005;
    float nms = .45;

    int nthreads = 4;
    image *val = calloc(nthreads, sizeof(image));
    image *val_resized = calloc(nthreads, sizeof(image));
    image *buf = calloc(nthreads, sizeof(image));
    image *buf_resized = calloc(nthreads, sizeof(image));
    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));

    load_args args = {0};
    args.w = net->w;
    args.h = net->h;
    //args.type = IMAGE_DATA;
    args.type = LETTERBOX_DATA;

    for(t = 0; t < nthreads; ++t){
        args.path = paths[i+t];
        args.im = &buf[t];
        args.resized = &buf_resized[t];
        thr[t] = load_data_in_thread(args);
    }
    double start = what_time_is_it_now();
    for(i = nthreads; i < m+nthreads; i += nthreads){
        fprintf(stderr, "%d\n", i);
        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
            pthread_join(thr[t], 0);
            val[t] = buf[t];
            val_resized[t] = buf_resized[t];
        }
        for(t = 0; t < nthreads && i+t < m; ++t){
            args.path = paths[i+t];
            args.im = &buf[t];
            args.resized = &buf_resized[t];
            thr[t] = load_data_in_thread(args);
        }
        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
            char *path = paths[i+t-nthreads];
            char *id = basecfg(path);
            float *X = val_resized[t].data;
            network_predict(net, X);
            int w = val[t].w;
            int h = val[t].h;
            int nboxes = 0;
            detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &nboxes);
            if (nms) do_nms_sort(dets, nboxes, classes, nms);
            if (coco){
                print_cocos(fp, path, dets, nboxes, classes, w, h);
            } else if (imagenet){
                print_imagenet_detections(fp, i+t-nthreads+1, dets, nboxes, classes, w, h);
            } else {
                print_detector_detections(fps, id, dets, nboxes, classes, w, h);
            }
            free_detections(dets, nboxes);
            free(id);
            free_image(val[t]);
            free_image(val_resized[t]);
        }
    }
    for(j = 0; j < classes; ++j){
        if(fps) fclose(fps[j]);
    }
    if(coco){
        fseek(fp, -2, SEEK_CUR); 
        fprintf(fp, "\n]\n");
        fclose(fp);
    }
    fprintf(stderr, "Total Detection Time: %f Seconds\n", what_time_is_it_now() - start);
}

void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
    srand(time(0));

    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "/home/xieqi/project/traffic_object_detection/voc_data/test.txt");
    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

    //layer l = net->layers[net->n-1];

    int j, k;

    int m = plist->size;    //测试的图片总数
    int i=0;

    float thresh = .001;
    float iou_thresh = .5;
    float nms = .4;

    int total = 0;      //实际有多少个bbox
    int correct = 0;    //正确识别出了多少个bbox
    int proposals = 0;  //测试集预测的bbox总数
    float avg_iou = 0;
    
    //printf("l.w*l.h*l.n = %d\n",l.w*l.h*l.n);
    for(i = 0; i < m; ++i){
        char *path = paths[i];
        image orig = load_image_color(path, 0, 0);
        image sized = resize_image(orig, net->w, net->h);
        char *id = basecfg(path);
        network_predict(net, sized.data);
        int nboxes = 0;
        detection *dets = get_network_boxes(net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes);
        if (nms) do_nms_obj(dets, nboxes, 1, nms);

        char labelpath[4096];
        find_replace(path, "images", "labels", labelpath);
        find_replace(labelpath, "JPEGImages", "labels", labelpath);
        find_replace(labelpath, ".jpg", ".txt", labelpath);
        find_replace(labelpath, ".JPEG", ".txt", labelpath);

        int num_labels = 0;     //测试集实际的标注框数量
        box_label *truth = read_boxes(labelpath, &num_labels);
        for(k = 0; k < nboxes; ++k){
            if(dets[k].objectness > thresh){
                ++proposals;
            }
        }
        for (j = 0; j < num_labels; ++j) {
            ++total;
            box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
            //printf("truth x=%f, y=%f, w=%f,h=%f\n",truth[j].x,truth[j].y,truth[j].w,truth[j].h);
            float best_iou = 0;
            //对每一个标注的框
            for(k = 0; k < nboxes; ++k){
            //for(k = 0; k < l.w*l.h*l.n; ++k){
                float iou = box_iou(dets[k].bbox, t);
                //printf("predict=%f iou=%f x=%f, y=%f, w=%f,h=%f\n",dets[k].objectness,iou,dets[k].bbox.x,dets[k].bbox.y,dets[k].bbox.w,dets[k].bbox.h);
                if(dets[k].objectness > thresh && iou > best_iou){
                    best_iou = iou;
                }
            }
            //printf("best_iou=%f\n",best_iou);
            avg_iou += best_iou;
            if(best_iou > iou_thresh){
                ++correct;
            }
        }
        fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
        free(id);
        free_image(orig);
        free_image(sized);
    }
}

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); //options存储分类的标签等基本训练信息
    char *name_list = option_find_str(options, "names", "data/names.list"); //抽取标签名称
    char **names = get_labels(name_list);

    image **alphabet = load_alphabet(); //加载位于data/labels下的字符图片，用于显示矩形框名称
    network *net = load_network(cfgfile, weightfile, 0);    //用netweork.h中自定义的network结构体存储模型文件,函数位于parser.c
    set_batch_network(net, 1);
    srand(2222222);
    double start_time;
    double end_time;
    double img_time;
    double sum_time=0.0;
    char buff[256];
    char *input = buff;
    float nms=.45;
    int i=0;
    while(1){
        //读取结构对应的权重文件
        if(filename){
            strncpy(input, filename, 256);
            image im = load_image_color(input,0,0);
            image sized = letterbox_image(im, net->w, net->h);  //输入图片大小经过resize至输入大小
            //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;  //X指向图片的data元素，即图片像素
            start_time=what_time_is_it_now();
            network_predict(net, X);    //network_predict函数负责预测当前图片的数据X
            end_time=what_time_is_it_now();
            img_time= end_time - start_time;

            printf("%s: Predicted in %f seconds.\n", input, img_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");
                char image[2048];
                sprintf(image,"./data/predict/%s",GetFilename(filename));
                save_image(im,image);
                printf("predict %s successfully!\n",GetFilename(filename));
#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/xieqi/darknet/data/out_img",0)==-1)//修改成自己的路径
            {
              if (mkdir("/home/xieqi/darknet/data/out_img",0777))//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);  //输入图片大小经过resize至输入大小
                //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;  //X指向图片的data元素，即图片像素
                start_time = what_time_is_it_now();
                network_predict(net, X);    //network_predict函数负责预测当前图片的数据X
                end_time = what_time_is_it_now();
                img_time = end_time - start_time;
                sum_time = sum_time+img_time;
                printf("Try Very Hard:");
                printf("%s: Predicted in %f seconds.\n", path, img_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/xieqi/darknet/data/out_img/%s",GetFilename(path));//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;
        }
        printf("fps: %.2f   totall image %d\n",(float)m/sum_time,m);
      }
    }
}

/*
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;
    while(1){
        if(filename){
            strncpy(input, filename, 256);
        } else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        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
            make_window("predictions", 512, 512, 0);
            show_image(im, "predictions", 0);
#endif
        }

        free_image(im);
        free_image(sized);
        if (filename) break;
    }
}


void censor_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
{
#ifdef OPENCV
    char *base = basecfg(cfgfile);
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);

    srand(2222222);
    CvCapture * cap;

    int w = 1280;
    int h = 720;

    if(filename){
        cap = cvCaptureFromFile(filename);
    }else{
        cap = cvCaptureFromCAM(cam_index);
    }

    if(w){
        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
    }
    if(h){
        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
    }

    if(!cap) error("Couldn't connect to webcam.\n");
    cvNamedWindow(base, CV_WINDOW_NORMAL); 
    cvResizeWindow(base, 512, 512);
    float fps = 0;
    int i;
    float nms = .45;

    while(1){
        image in = get_image_from_stream(cap);
        //image in_s = resize_image(in, net->w, net->h);
        image in_s = letterbox_image(in, net->w, net->h);
        layer l = net->layers[net->n-1];

        float *X = in_s.data;
        network_predict(net, X);
        int nboxes = 0;
        detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 0, &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);

        for(i = 0; i < nboxes; ++i){
            if(dets[i].prob[class] > thresh){
                box b = dets[i].bbox;
                int left  = b.x-b.w/2.;
                int top   = b.y-b.h/2.;
                censor_image(in, left, top, b.w, b.h);
            }
        }
        show_image(in, base);
        cvWaitKey(10);
        free_detections(dets, nboxes);


        free_image(in_s);
        free_image(in);


        float curr = 0;
        fps = .9*fps + .1*curr;
        for(i = 0; i < skip; ++i){
            image in = get_image_from_stream(cap);
            free_image(in);
        }
    }
    #endif
}

void extract_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
{
#ifdef OPENCV
    char *base = basecfg(cfgfile);
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);

    srand(2222222);
    CvCapture * cap;

    int w = 1280;
    int h = 720;

    if(filename){
        cap = cvCaptureFromFile(filename);
    }else{
        cap = cvCaptureFromCAM(cam_index);
    }

    if(w){
        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
    }
    if(h){
        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
    }

    if(!cap) error("Couldn't connect to webcam.\n");
    cvNamedWindow(base, CV_WINDOW_NORMAL); 
    cvResizeWindow(base, 512, 512);
    float fps = 0;
    int i;
    int count = 0;
    float nms = .45;

    while(1){
        image in = get_image_from_stream(cap);
        //image in_s = resize_image(in, net->w, net->h);
        image in_s = letterbox_image(in, net->w, net->h);
        layer l = net->layers[net->n-1];

        show_image(in, base);

        int nboxes = 0;
        float *X = in_s.data;
        network_predict(net, X);
        detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 1, &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);

        for(i = 0; i < nboxes; ++i){
            if(dets[i].prob[class] > thresh){
                box b = dets[i].bbox;
                int size = b.w*in.w > b.h*in.h ? b.w*in.w : b.h*in.h;
                int dx  = b.x*in.w-size/2.;
                int dy  = b.y*in.h-size/2.;
                image bim = crop_image(in, dx, dy, size, size);
                char buff[2048];
                sprintf(buff, "results/extract/%07d", count);
                ++count;
                save_image(bim, buff);
                free_image(bim);
            }
        }
        free_detections(dets, nboxes);


        free_image(in_s);
        free_image(in);


        float curr = 0;
        fps = .9*fps + .1*curr;
        for(i = 0; i < skip; ++i){
            image in = get_image_from_stream(cap);
            free_image(in);
        }
    }
    #endif
}
*/

/*
void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets)
{
    network_predict_image(net, im);
    layer l = net->layers[net->n-1];
    int nboxes = num_boxes(net);
    fill_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 0, dets);
    if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
}
*/

//  ./darknet [xxx]中如果命令如果第二个xxx参数是detector，则调用这个
void run_detector(int argc, char **argv)
{
    char *prefix = find_char_arg(argc, argv, "-prefix", 0); //寻找是否有参数prefix, 默认参数0，argv为二维数组，存储了参数字符串
    float thresh = find_float_arg(argc, argv, "-thresh", .5);   //寻找是否有参数thresh，thresh为输出的阈值,默认参数0.24
    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);    //寻找是否有参数hier_thresh,默认0.5
    int cam_index = find_int_arg(argc, argv, "-c", 0);  //寻找是否有参数c，默认0
    int frame_skip = find_int_arg(argc, argv, "-s", 0); //寻找是否有参数s，默认0
    int avg = find_int_arg(argc, argv, "-avg", 3);
    
    //如果输入参数小于4个，输出正确语法如何使用
    //printf 等价于 fprintf(stdout, ...)，这里stderr和stdout默认输出设备都是屏幕，但是stderr一般指标准出错输入设备 
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
    }
    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); //寻找是否有参数gpus，默认0
    char *outfile = find_char_arg(argc, argv, "-out", 0);   //检查是否指定GPU运算,默认0
    int *gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if(gpu_list){
        printf("%s\n", gpu_list);
        int len = strlen(gpu_list);
        ngpus = 1;
        int i;
        for(i = 0; i < len; ++i){
            if (gpu_list[i] == ',') ++ngpus;
        }
        gpus = calloc(ngpus, sizeof(int));
        for(i = 0; i < ngpus; ++i){
            gpus[i] = atoi(gpu_list);
            gpu_list = strchr(gpu_list, ',')+1;
        }
    } else {
        gpu = gpu_index;
        gpus = &gpu;
        ngpus = 1;
    }

    int clear = find_arg(argc, argv, "-clear"); //检查clear参数
    int fullscreen = find_arg(argc, argv, "-fullscreen");
    int width = find_int_arg(argc, argv, "-w", 0);
    int height = find_int_arg(argc, argv, "-h", 0);
    int fps = find_int_arg(argc, argv, "-fps", 0);
    //int class = find_int_arg(argc, argv, "-class", 0);

    char *datacfg = argv[3];   //存data文件路径
    char *cfg = argv[4];    //存cfg文件路径
    char *weights = (argc > 5) ? argv[5] : 0;   //存weight文件路径
    char *filename = (argc > 6) ? argv[6]: 0;   //存待检测文件路径
    
    //根据第三个参数的内容，调用不同的函数，并传入之前解析的参数
    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);
    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
    else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
    else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);
    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
    else if(0==strcmp(argv[2], "demo")) {
        list *options = read_data_cfg(datacfg);
        int classes = option_find_int(options, "classes", 20);
        char *name_list = option_find_str(options, "names", "data/names.list");
        char **names = get_labels(name_list);
        demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);
    }
    //else if(0==strcmp(argv[2], "extract")) extract_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
    //else if(0==strcmp(argv[2], "censor")) censor_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
}

"""
Note:If during training you see nan values for avg (loss) field - then training goes wrong,
but if nan is in some other lines - then training goes well.

When should I stop training:
When you see that average loss 0.xxxxxx avg no longer decreases at many iterations then you should stop training.
Once training is stopped, you should take some of last .weights-files from darknet\build\darknet\x64\backup and choose the best of them.

Overfitting - is case when you can detect objects on images from training-dataset,
but can't detect objects on any others images. You should get weights from Early Stopping Point.

IoU (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24

How to improve object detection:
Before training:
set flag random=1 in your .cfg-file - it will increase precision by training Yolo for different resolutions.
increase network resolution in your .cfg-file (height=608, width=608 or any value multiple of 32) - it will increase precision.
recalculate anchors for your dataset for width and height from cfg-file:
darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416 then set the same 9 anchors in each of 3 [yolo]-layers in your cfg-file
设置锚点

desirable that your training dataset include images with objects at diffrent:
scales, rotations, lightings, from different sides, on different backgrounds
样本特点尽量多样化，亮度，旋转，背景，目标位置，尺寸

desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty .txt files)
可以添加没有标注框的图片和其空的txt文件，作为negative数据

for training with a large number of objects in each image, add the parameter max=200 or higher value in the last layer [region] in your cfg-file

to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning,
set param stopbackward=1 in one of the penultimate convolutional layers before the 1-st [yolo]-layer,
for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598
可以在第一个[yolo]层之前的倒数第二个[convolutional]层末尾添加 stopbackward=1,以此提升训练速度

After training - for detection:
Increase network-resolution by set in your .cfg-file (height=608 and width=608) or (height=832 and width=832)
or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects,
you do not need to train the network again, just use .weights-file already trained for 416x416 resolution.
即使在用416*416训练完之后，也可以在cfg文件中设置较大的width和height，增加网络对图像的分辨率，从而更可能检测出图像中的小目标，而不需要重新训练

if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64

"""

posted @ 2018-10-19 22:38 chiemon 阅读(34468) 评论(0) 编辑收藏举报

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蒙太琦

YOLO-V3实战（darknet）

一. 准备工作

1）实验环境：

3）配置darknet编译环境：

4) 实验环境测试

5) ./darknet编译格式

二. 实验步骤

1）下载 Pascal VOC 2007 和 2012 的数据集

2）生成 VOC 数据集的标签

3）修改配置文件

Ⅰ. 数据配置文件——cfg/voc.data

Ⅱ. 模型配置文件——cfg/yolov3-voc.cfg

Ⅲ. 标签配置文件——data/voc.names

Ⅳ. 修改数据格式——scr/data.c

4）下载预训练的参数（卷积权重）

5）训练模型：

Ⅰ. 单GPU训练: ./darknet detector train <data_cfg> <train_cfg> <weights> -gpu <gpu_id>

Ⅱ. 多GPU训练: ./darknet detector train <data_cfg> <model_cfg> <weights> -gpus <gpu_list>

Ⅲ. 从checkpoint继续训练

Ⅳ. CPU训练:

Ⅴ. 生成loss-iter曲线

6）训练过程：

三. 模型评价

1）更改配置文件：

Ⅰ. 模型参数(cfg/yoloc3.cfg)：

Ⅱ. 更改为批处理(example/detector.c)：

①. 在detector.c中增加头文件：

②. 在前面添加GetFilename(char *fullname)函数

③. 用下面代码替换detector.c文件的void test_detector函数（注意有3处要改成自己的路径）

④. validate_detector_recall函数定义和调用改为：

⑤.validate_detector_recall内的plist和paths的如下初始化代码：

Ⅲ. 在darknet下重新make

2). 测试单张图片

3). 生成预测结果

Ⅰ. 批量测试——输出文本检测结果

Ⅱ. 批量测试——输出图片检测结果

4). 计算recall（执行这个命令需要修改detector.c文件，修改信息请参考“detector.c修改”）

error：

5). 计算AP、mAP

Ⅰ. 下载voc_eval.py到 darknet 的根目录

Ⅱ. 计算单类AP

①. 在darknet根目录下新建computer_mAP.py

②. 用python2执行computer_mAP.py

Ⅲ. 计算总mAP

四、文件解析

1). ~detector.c

　　Ⅰ. 数据配置文件——cfg/voc.data

　　Ⅱ. 模型配置文件——cfg/yolov3-voc.cfg

　　Ⅲ. 标签配置文件——data/voc.names

　　 Ⅳ. 修改数据格式——scr/data.c

　　Ⅰ. 单GPU训练: ./darknet detector train <data_cfg> <train_cfg> <weights> -gpu <gpu_id>

　　Ⅱ. 多GPU训练: ./darknet detector train <data_cfg> <model_cfg> <weights> -gpus <gpu_list>

　　Ⅲ. 从checkpoint继续训练

　　Ⅳ. CPU训练:

　　Ⅴ. 生成loss-iter曲线

　　Ⅰ. 模型参数(cfg/yoloc3.cfg)：

　　Ⅱ. 更改为批处理(example/detector.c)：

　　　　①. 在detector.c中增加头文件：

　　　　②. 在前面添加GetFilename(char *fullname)函数

　　　　③. 用下面代码替换detector.c文件的void test_detector函数（注意有3处要改成自己的路径）

　　　　④. `validate_detector_recall`函数定义和调用改为：

　　　　`⑤.validate_detector_recall`内的`plist`和`paths`的如下初始化代码：

　Ⅲ. 在darknet下重新make

　　Ⅰ. 批量测试——输出文本检测结果

　　Ⅱ. 批量测试——输出图片检测结果

　　error：

　　Ⅰ. 下载voc_eval.py到 darknet 的根目录

　　Ⅱ. 计算单类AP

　　　　①. 在darknet根目录下新建computer_mAP.py

　　　　②. 用python2执行computer_mAP.py

　　Ⅲ. 计算总mAP