Yolo v3的使用方法
參考自@zhaonan
目錄
安裝darknet
- 下載庫文件
git clone https://github.com/pjreddie/darknet
cd darknet
- 修改Makefile
GPU=1 #0或1
CUDNN=1 #0或1
OPENCV=0 #0或1
OPENMP=0
DEBUG=0
- 編譯
make
- 下載預訓練模型
wget https://pjreddie.com/media/files/yolov3.weights
- 用預訓練模型進行簡單的測試
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
訓練Pascal VOC格式的數據
- 生成Labels，因為darknet不需要xml文件，需要.txt文件（格式：
）
用voc_label.py（位於./scripts）cat voc_label.py 共修改四處
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] #替換為自己的數據集
classes = ["head", "eye", "nose"] #修改為自己的類別
def convert(size, box):
dw = 1./(size[0])
dh = 1./(size[1])
x = (box[0] + box[1])/2.0 - 1
y = (box[2] + box[3])/2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(year, image_id):
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) #將數據集放於當前目錄下
out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult)==1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
convert_annotation(year, image_id)
list_file.close()
os.system("cat 2007_train.txt 2007_val.txt > train.txt") #修改為自己的數據集用作訓練
wget https://pjreddie.com/media/files/voc_label.py
python voc_label.py
在VOCdevkit/VOC2007/labels/中：
learner@learner-pc:~/darknet/scripts$ ls 2007_test.txt #0 dice_label.sh imagenet_label.sh VOCdevkit_original 2007_train.txt #1 gen_tactic.sh train.txt #3 voc_label.py 2007_val.txt #2 get_coco_dataset.sh VOCdevkit
這時darknet需要一個txt文件，其中包含了所有的圖片
cat 2007_train.txt 2007_val.txt 2012_*.txt > train.txt
修改cfg文件中的voc.data
classes= 3 #修改為自己的類別數
train = /home/learner/darknet/data/voc/train.txt #修改為自己的路徑 or /home/learner/darknet/scripts/2007_test.txt
valid = /home/learner/darknet/data/voc/2007_test.txt #修改為自己的路徑 or /home/learner/darknet/scripts/2007_test.txt
names = /home/learner/darknet/data/voc.names #修改見voc.names
backup = /home/learner/darknet/backup #修改為自己的路徑，輸出的權重信息將存儲其內
修改VOC.names
head #自己需要探測的類別，一行一個
eye
nose
下載預訓練卷積層權重
wget https://pjreddie.com/media/files/darknet53.conv.74
修改cfg/yolov3-voc.cfg
[net]
# Testing
batch=64
subdivisions=32 #每批訓練的個數=batch/subvisions，根據自己GPU顯存進行修改，顯存不夠改大一些
# Training
# batch=64
# subdivisions=16
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 50200 #訓練步數
policy=steps
steps=40000,45000 #開始衰減的步數
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
.....
[convolutional]
size=1
stride=1
pad=1
filters=24 #filters = 3 * ( classes + 5 ) here,filters=3*(3+5)
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=3 #修改為自己的類別數
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24 #filters = 3 * ( classes + 5 ) here,filters=3*(3+5)
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=3 #修改為自己的類別數
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24 #filters = 3 * ( classes + 5 ) here,filters=3*(3+5)
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=3 #修改為自己的類別數
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
訓練自己的模型
1 單GPU訓練：./darknet -i <gpu_id> detector train <data_cfg> <train_cfg> <weights>
./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74
2 多GPU訓練，格式為0,1,2,3：./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
測試Yolo模型
測試單張圖片：
- 測試單張圖片，需要編譯時有OpenCV支持：
./darknet detector test <data_cfg> <test_cfg> <weights> <image_file> #本次測試無opencv支持 <test_cfg>文件中batch和subdivisions兩項必須為1。- 測試時還可以用
-thresh和-hier選項指定對應參數。 ./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_20000.weights Eminem.jpg
批量測試圖片
-
yolov3-voc.cfg(cfg文件夾下)文件中batch和subdivisions兩項必須為1。 -
在detector.c中增加頭文件：
#include <unistd.h> /* Many POSIX functions (but not all, by a large margin) */ #include <fcntl.h> /* open(), creat() - and fcntl() */ -
在前面添加*GetFilename(char *p)函數
#include "darknet.h" #include <sys/stat.h> //需增加的頭文件 #include<stdio.h> #include<time.h> #include<sys/types.h> //需增加的頭文件 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 *p) { static char name[30]={""}; char *q = strrchr(p,'/') + 1; strncpy(name,q,20); return name; } -
用下面代碼替換detector.c文件（example文件夾下）的void test_detector函數（注意有3處要改成自己的路徑）
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",0);
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/learner/darknet/data/outv3tiny_dpj",0)==-1)//"/home/learner/darknet/data"修改成自己的路徑
{
if (mkdir("/home/learner/darknet/data/outv3tiny_dpj",0777))//"/home/learner/darknet/data"修改成自己的路徑
{
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/learner/darknet/data/outv3tiny_dpj/%s",GetFilename(path));//"/home/leaner/darknet/data"修改成自己的路徑
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;
}
}
}
}
- 重新進行編譯
make clean
make
- 開始批量測試
./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_20000.weights
- 輸入Image Path(所有的測試文件的路徑，可以復制voc.data中valid后邊的路徑):
/home/learner/darknet/data/voc/2007_test.txt # 完整路徑
- 結果都保存在
./data/out文件夾下
生成預測結果
生成預測結果
./darknet detector valid <data_cfg> <test_cfg> <weights> <out_file>- yolov3-voc.cfg(cfg文件夾下)
文件中batch和subdivisions兩項必須為1。 - 結果生成在
<data_cfg>的results指定的目錄下以<out_file>開頭的若干文件中，若<data_cfg>沒有指定results，那么默認為<darknet_root>/results。 - 執行語句如下：在終端只返回用時，在./results/comp4_det_test_[類名].txt里保存測試結果
./darknet detector valid cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_20000.weights
采用第三方compute_mAP
下載第三方庫：
git clone https://github.com/LianjiLi/yolo-compute-map.git
進行如下修改:
-
修改darknet/examples/detector.c中validate_detector()
char *valid_images = option_find_str(options, "valid", "./data/2007_test.txt");//改成自己的測試文件路徑 if(!outfile) outfile = "comp4_det_test_"; fps = calloc(classes, sizeof(FILE *)); for(j = 0; j < classes; ++j){ snprintf(buff, 1024, "%s/%s.txt", prefix, names[j]);//刪除outfile參數以及對應的%s fps[j] = fopen(buff, "w"); -
重新編譯
make clean make -
運行valid
darknet文件夾下運行./darknet detector valid cfg/voc.data cfg/yolov3-tiny.cfg backup/yolov3-tiny_164000.weights（改為自己的模型路徑） -
在本文件夾下運行
python compute_mAP.py -
說明：compute_mAP.py中的test.txt文件內容只有文件名字，不帶絕對路徑，不帶后綴
高級進階
darknet的淺層特征可視化請參看：https://www.cnblogs.com/pprp/p/10146355.html
AlexyAB大神總結的優化經驗請參看：https://www.cnblogs.com/pprp/p/10204480.html
如何使用Darknet進行分類請參看：https://www.cnblogs.com/pprp/p/10342335.html
Darknet loss可視化軟件請參看：https://www.cnblogs.com/pprp/p/10248436.html
如何設計更改YOLO網絡結構：https://pprp.github.io/2018/09/20/tricks.html
YOLO詳細改進總結：https://pprp.github.io/2018/06/20/yolo.html
ps: 以上都是自己科研過程中總結內容，可能不夠系統，歡迎留言討論
Reference
轉載請注明作者 ^_^, 如有問題請留言。