概述
准备训练数据
使用darknet训练自己的YOLO模型需要将数据转成darknet需要的格式,每张图片对应一个.txt的label文件,文件格式如下:
<object-class> <x> <y> <width> <height>
object-class是类的索引,后面的4个值都是相对于整张图片的比例。
x是ROI中心的x坐标,y是ROI中心的y坐标,width是ROI的宽,height是ROI的高。
我需要用到Pascal VOC、MSCOCO、ImageNet和自己标记的一些图片。
混用这些数据集有一个严重的问题,有一些需要标记的物体没有被标记。
如ImageNet的200种物体中有iPod并做了标记,而MSCOCO中有一些图片中有iPod却没有标记出来,这会导致模型的精度下降。该问题可以通过对这部分图片重新标记来解决(工作量很大);也可以修改损失函数,对不同数据集的image计算不同的损失,同时针对不同数据集中的数据使用不同的object_scale和noobject_scale。
整合这些数据集首先要准备一个list,list中列出了要识别的物体。
如paul_list.txt
0,ambulance
1,apple
2,automat
3,backpack
4,baggage
5,banana
6,baseball
7,basketball
8,bed
9,bench
转换Pascal VOC
darknet作者提供了voc_label.py脚本来实现该功能,我们只需修改脚本中的classes为我们需要的classes即可,然后在VOCdevkit的父目录执行voc_label.py即可。
classes = ["ambulance", "apple", "automat", "backpack", "baggage", "banana", "baseball", "basketball", "bed","bench"]
转换MSCOCO
查看coco的80种物体有哪些是我们需要的,制作coco_list.txt,格式为,。如:
1,apple
3,backpack
5,banana
8,bed
9,bench
安装MSCOCO提供的python API库,然后执行coco_label.py。
coco_label.py见github。
https://github.com/PaulChongPeng/darknet/blob/master/tools/coco_label.py
执行脚本前需要修改dataDir和classes为自己的COCO数据集路径和coco_list.txt路径
# coding=utf-8
# 使用说明
# 需要先安装coco tools
# git clone https://github.com/pdollar/coco.git
# cd coco/PythonAPI
# make install(可能会缺少相关依赖,根据提示安装依赖即可)
# 执行脚本前需在train2014和val2014目录下分别创建JPEGImages和labels目录,并将原来train2014和val2014目录下的图片移到JPEGImages下
# COCO数据集的filelist目录下会生成图片路径列表
# COCO数据集的子集的labels目录下会生成yolo需要的标注文件
from pycocotools.coco import COCO
import shutil
import os
# 将ROI的坐标转换为yolo需要的坐标
# size是图片的w和h
# box里保存的是ROI的坐标(x,y的最大值和最小值)
# 返回值为ROI中心点相对于图片大小的比例坐标,和ROI的w、h相对于图片大小的比例
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = box[0] + box[2] / 2.0
y = box[1] + box[3] / 2.0
w = box[2]
h = box[3]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)
# 获取所需要的类名和id
# path为类名和id的对应关系列表的地址(标注文件中可能有很多类,我们只加载该path指向文件中的类)
# 返回值是一个字典,键名是类名,键值是id
def get_classes_and_index(path):
D = {}
f = open(path)
for line in f:
temp = line.rstrip().split(',', 2)
print("temp[0]:" + temp[0] + "n")
print("temp[1]:" + temp[1] + "n")
D[temp[1]] = temp[0]
return D
dataDir = '/mnt/large4t/pengchong_data/Data/COCO' # COCO数据集所在的路径
dataType = 'train2014' # 要转换的COCO数据集的子集名
annFile = '%s/annotations/instances_%s.json' % (dataDir, dataType) # COCO数据集的标注文件路径
classes = get_classes_and_index('/mnt/large4t/pengchong_data/Tools/Yolo_paul/darknet/data/coco_list.txt')
# labels 目录若不存在,创建labels目录。若存在,则清空目录
if not os.path.exists('%s/%s/labels/' % (dataDir, dataType)):
os.makedirs('%s/%s/labels/' % (dataDir, dataType))
else:
shutil.rmtree('%s/%s/labels/' % (dataDir, dataType))
os.makedirs('%s/%s/labels/' % (dataDir, dataType))
# filelist 目录若不存在,创建filelist目录。
if not os.path.exists('%s/filelist/' % dataDir):
os.makedirs('%s/filelist/' % dataDir)
coco = COCO(annFile) # 加载解析标注文件
list_file = open('%s/filelist/%s.txt' % (dataDir, dataType), 'w') # 数据集的图片list保存路径
imgIds = coco.getImgIds() # 获取标注文件中所有图片的COCO Img ID
catIds = coco.getCatIds() # 获取标注文件总所有的物体类别的COCO Cat ID
for imgId in imgIds:
objCount = 0 # 一个标志位,用来判断该img是否包含我们需要的标注
print('imgId :%s' % imgId)
Img = coco.loadImgs(imgId)[0] # 加载图片信息
print('Img :%s' % Img)
filename = Img['file_name'] # 获取图片名
width = Img['width'] # 获取图片尺寸
height = Img['height'] # 获取图片尺寸
print('filename :%s, width :%s ,height :%s' % (filename, width, height))
annIds = coco.getAnnIds(imgIds=imgId, catIds=catIds, iscrowd=None) # 获取该图片对应的所有COCO物体类别标注ID
print('annIds :%s' % annIds)
for annId in annIds:
anns = coco.loadAnns(annId)[0] # 加载标注信息
catId = anns['category_id'] # 获取该标注对应的物体类别的COCO Cat ID
cat = coco.loadCats(catId)[0]['name'] # 获取该COCO Cat ID对应的物体种类名
# print 'anns :%s' % anns
# print 'catId :%s , cat :%s' % (catId,cat)
# 如果该类名在我们需要的物体种类列表中,将标注文件转换为YOLO需要的格式
if cat in classes:
objCount = objCount + 1
out_file = open('%s/%s/labels/%s.txt' % (dataDir, dataType, filename[:-4]), 'a')
cls_id = classes[cat] # 获取该类物体在yolo训练中的id
box = anns['bbox']
size = [width, height]
bb = convert(size, box)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + 'n')
out_file.close()
if objCount > 0:
list_file.write('%s/%s/JPEGImages/%sn' % (dataDir, dataType, filename))
list_file.close()
转换ImageNet
我使用的是ILSVRC2016的数据,查看200种物体中有哪些是我们需要的,然后制作imagenet_list.txt。
需要注意,ImageNet的标注文件中的object name使用的物体的WordNetID,所以imagenet_list.txt中需要使用WordNetID,如:
1,n07739125
3,n02769748
5,n07753592
6,n02799071
7,n02802426
9,n02828884
为了方便获取WordNetID在ImageNet中的物体名词(paul_list.txt中的名词未必和ImageNet中的一致),可以制作一个imagenet_map.txt,如:
1,apple,n07739125
3,backpack,n02769748
5,banana,n07753592
6,baseball,n02799071
7,basketball,n02802426
9,bench,n02828884
制作imagenet_list.txt和imagenet_map.txt需要知道WordNetID和名词间的映射关系,有两个办法。
离线版:
从ImageNet下载words.txt(WordNetID和名词间的映射)和gloss.txt(WordNetID对应的名词的定义),然后查询。如果没有梯子,国内访问ImageNet龟速,文件被我备份在GitHub。
https://github.com/PaulChongPeng/darknet/blob/32dddd8509de4bf57cad0aa330160d57d33d0c66/data/words.txt
https://github.com/PaulChongPeng/darknet/blob/32dddd8509de4bf57cad0aa330160d57d33d0c66/data/gloss.txt
在线版:
访问 http://image-net.org/challenges/LSVRC/2015/browse-det-synsets 。请自备梯子,不然慢的令人发指。
点击需要查询的名词,如Volleyball,会跳转到对应的网页,我们需要的是网页地址后的wnid。如 http://imagenet.stanford.edu/synset?wnid=n04540053 。
制作好list后,将imagenet_to_yolo.py放在ILSVRC2016/bject_detection/ILSVRC目录下,并将Data文件夹重命名为JPEGImages(因为darknet找图片对应的标记文件是直接替换JPEGImages为labels,图片后缀名替换为txt)。修改classes为自己的list路径后直接运行脚本即可。
imagenet_to_yolo.py 我放在了GitHub上:
https://github.com/PaulChongPeng/darknet/blob/master/tools/imagenet_to_yolo.py
# coding=utf-8
# 使用说明
# 将该文件放在ILSVRC2016/bject_detection/ILSVRC目录下,并将Data文件夹重命名为JPEGImages
# 执行该工具,Lists目录下会生成图片路径列表
# labels目录下会生成yolo需要的标注文件
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import shutil
# 获取所有包含标注文件的的目录路径
def get_dirs():
dirs = ['DET/train/ILSVRC2014_train_0006', 'DET/train/ILSVRC2014_train_0005', 'DET/train/ILSVRC2014_train_0004',
'DET/train/ILSVRC2014_train_0003', 'DET/train/ILSVRC2014_train_0002', 'DET/train/ILSVRC2014_train_0001',
'DET/train/ILSVRC2014_train_0000', 'DET/val']
dirs_2013 = os.listdir('JPEGImages/DET/train/ILSVRC2013_train/')
for dir_2013 in dirs_2013:
dirs.append('DET/train/ILSVRC2013_train/' + dir_2013)
return dirs
# 获取所需要的类名和id
# path为类名和id的对应关系列表的地址(标注文件中可能有很多类,我们只加载该path指向文件中的类)
# 返回值是一个字典,键名是类名,键值是id
def get_classes_and_index(path):
D = {}
f = open(path)
for line in f:
temp = line.rstrip().split(',', 2)
D[temp[1]] = temp[0]
return D
# 将ROI的坐标转换为yolo需要的坐标
# size是图片的w和h
# box里保存的是ROI的坐标(x,y的最大值和最小值)
# 返回值为ROI中心点相对于图片大小的比例坐标,和ROI的w、h相对于图片大小的比例
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
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)
# 将labelImg 生成的xml文件转换为yolo需要的txt文件
# image_dir 图片所在的目录的路径
# image_id图片名
def convert_annotation(image_dir, image_id):
in_file = open('Annotations/%s/%s.xml' % (image_dir, image_id))
obj_num = 0 # 一个标志位,用来判断该img是否包含我们需要的标注
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'):
cls = obj.find('name').text
if cls not in classes:
continue
obj_num = obj_num + 1
if obj_num == 1:
out_file = open('labels/%s/%s.txt' % (image_dir, image_id), 'w')
cls_id = classes[cls] # 获取该类物体在yolo训练中的id
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')
if obj_num > 0:
list_file = open('Lists/%s.txt' % image_dir.split('/')[-1], 'a') # 数据集的图片list保存路径
list_file.write('%s/JPEGImages/%s/%s.JPEGn' % (wd, image_dir, image_id))
list_file.close()
def IsSubString(SubStrList, Str):
flag = True
for substr in SubStrList:
if not (substr in Str):
flag = False
return flag
# 获取FindPath路径下指定格式(FlagStr)的文件名(不包含后缀名)列表
def GetFileList(FindPath, FlagStr=[]):
import os
FileList = []
FileNames = os.listdir(FindPath)
if (len(FileNames) > 0):
for fn in FileNames:
if (len(FlagStr) > 0):
if (IsSubString(FlagStr, fn)):
FileList.append(fn[:-4])
else:
FileList.append(fn)
if (len(FileList) > 0):
FileList.sort()
return FileList
classes = get_classes_and_index('/mnt/large4t/pengchong_data/Tools/Yolo_paul/darknet/data/imagenet_list.txt')
dirs = get_dirs()
wd = getcwd()
# Lists 目录若不存在,创建Lists目录。若存在,则清空目录
if not os.path.exists('Lists/'):
os.makedirs('Lists/')
else:
shutil.rmtree('Lists/')
os.makedirs('Lists/')
for image_dir in dirs:
if not os.path.exists('JPEGImages/' + image_dir):
print("JPEGImages/%s dir not exist" % image_dir)
continue
# labels 目录若不存在,创建labels目录。若存在,则清空目录
if not os.path.exists('labels/%s' % (image_dir)):
os.makedirs('labels/%s' % (image_dir))
else:
shutil.rmtree('labels/%s' % (image_dir))
os.makedirs('labels/%s' % (image_dir))
image_ids = GetFileList('Annotations/' + image_dir, ['xml'])
for image_id in image_ids:
print(image_id)
convert_annotation(image_dir, image_id)
转换自己的数据
我使用的labelImg工具做的图像标注,标记格式大体和VOC一致。
工具地址见GitHub: https://github.com/tzutalin/labelImg
只需要简单修改voc_label.py就可以转换自己的数据。修改后的脚本命名为lableImg_voc_to_yolo.py。我放在了GitHub上:
https://github.com/PaulChongPeng/darknet/blob/master/tools/lableImg_voc_to_yolo.py
# coding=utf-8
# 使用说明
# 要转换的数据集目录结构为:
# Paul/time/class/annotations/xml文件
# Paul/time/class/images/jpg文件
# Paul/time/class/labels/即将生成的yolo需要的txt文件
# 该文件需放在Paul目录下,该目录下将会生成名为“日期”的txt文件,文件内容为日期文件夹下所有图片的路径
# 有多少个日期的文件夹,就将多少个文件夹的名字加入sets
# 需要生成多少种物体的标签,就将多少种物体加入classes
# labels目录下生成的txt文件中的第一个数字就是物体种类在classes中的索引
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import shutil
sets = ['20170401', '20170414']
# 获取所需要的类名和id
# path为类名和id的对应关系列表的地址(标注文件中可能有很多类,我们只加载该path指向文件中的类)
# 返回值是一个字典,键名是类名,键值是id
def get_classes_and_index(path):
D = {}
f = open(path)
for line in f:
temp = line.rstrip().split(',', 2)
print("temp[0]:" + temp[0] + "n")
print("temp[1]:" + temp[1] + "n")
D[temp[1].replace(' ', '')] = temp[0]
return D
# 将ROI的坐标转换为yolo需要的坐标
# size是图片的w和h
# box里保存的是ROI的坐标(x,y的最大值和最小值)
# 返回值为ROI中心点相对于图片大小的比例坐标,和ROI的w、h相对于图片大小的比例
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
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)
# 将labelImg 生成的xml文件转换为yolo需要的txt文件
# path到类名一级的目录路径
# image_id图片名
def convert_annotation(path, image_id):
in_file = open('%s/annotations/%s.xml' % (path, image_id))
out_file = open('%s/labels/%s.txt' % (path, 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'):
cls = obj.find('name').text.replace(' ', '')
# 如果该类物体不在我们的yolo训练列表中,跳过
if cls not in classes:
continue
cls_id = classes[cls] # 获取该类物体在yolo训练列表中的id
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')
def IsSubString(SubStrList, Str):
flag = True
for substr in SubStrList:
if not (substr in Str):
flag = False
return flag
# 获取FindPath路径下指定格式(FlagStr)的文件名(不包含后缀名)列表
def GetFileList(FindPath, FlagStr=[]):
import os
FileList = []
FileNames = os.listdir(FindPath)
if (len(FileNames) > 0):
for fn in FileNames:
if (len(FlagStr) > 0):
if (IsSubString(FlagStr, fn)):
FileList.append(fn[:-4])
else:
FileList.append(fn)
if (len(FileList) > 0):
FileList.sort()
return FileList
# 获取目录下子目录的目录名列表
def get_dirs(time):
dirs = []
dirs_temp = os.listdir(time)
for dir_name in dirs_temp:
dirs.append(time + '/' + dir_name)
return dirs
wd = getcwd()
classes = get_classes_and_index('/raid/pengchong_data/Tools/Paul_YOLO/data/Paul_list.txt')
for time in sets:
dirs = get_dirs(time)
list_file = open('%s.txt' % time, 'w') # 数据集的图片list保存路径
for path in dirs:
print(path)
if not os.path.exists('%s/annotations/' % path):
os.makedirs('%s/annotations/' % path)
if not os.path.exists('%s/labels/' % path):
os.makedirs('%s/labels/' % path)
else:
shutil.rmtree('%s/labels/' % path)
os.makedirs('%s/labels/' % path)
image_ids = GetFileList(path + '/annotations/', ['xml'])
for image_id in image_ids:
print(image_id)
list_file.write('%s/%s/images/%s.jpgn' % (wd, path, image_id))
convert_annotation(path, image_id)
list_file.close()
将各个数据集的标注文件转换成YOLO需要的格式后,将脚本生成的图像地址list的内容全部拷贝到paul.txt中,然后使用partial.py脚本随机分割为train,val,test data。脚本已上传至GitHut,可根据自己的需要进行修改。
https://github.com/PaulChongPeng/darknet/blob/master/tools/partial.py
数据准备工作到此就算结束了。
准备配置文件
在cfg目录下添加paul.data,内容如下:
classes=10 要识别物体的种类数
train = data/paul_train.txt 训练集图片list
valid = data/paul_val.txt 验证集图片list
names = data/paul.names 要识别的物体list
backup = /mnt/large4t/pengchong_data/Tools/darknet/backup/ 训练时权重文件备份路径
在cfg目录下添加yolo-paul.cfg文件,该文件内容复制自默认的yolo-voc.cfg,根据自己的训练集和机器配置做修改,具体参数意义可以参考我之前的文章:
我修改的内容如下:
[net]
batch=27 每27张图更新一次权重,subdivisions=1时占用GPU memory 15.6G左右
......
......
learning_rate=0.00001 学习率大了容易发散
max_batches = 500000
......
......
[convolutional]
......
......
filters=75 最后一个卷积层输出的特征图数为5*(10+5)
......
......
[region]
......
......
classes=10 训练十种物体
......
......
在data目录下增加paul.names,内容如下:
ambulance
apple
automat
backpack
baggage
banana
baseball
basketball
bed
bench
修改Makefile
GPU=1
CUDNN=1
编译
make clean
make -j8
训练
首先准备ImageNet的预训练权重文件
curl -O https://pjreddie.com/media/files/darknet19.weights
使用前23层的权重
./darknet partial cfg/darknet19_448.cfg darknet19_448.weights darknet19_448.conv.23 23
partial命令可以分割权重文件,fine-tune的时候也会用到。
开始训练
./darknet detector train cfg/paul.data cfg/yolo-paul.cfg darknet19_448.conv.23 2>1 | tee paul_train_log.txt
剩下的就是等待了。
需要注意的是,如果学习率设置的比较大,训练结果很容易发散,训练过程输出的log会有nan字样,需要减小学习率后再进行训练。
多GPU训练技巧
darknet支持多GPU,使用多GPU训练可以极大加速训练速度。据我测试在DGX-1上使用8块Tesla P100同时训练的速度是在外星人上使用1块GTX1080的130多倍。
单GPU与多GPU的切换技巧
在darknet上使用多GPU训练需要一定技巧,盲目使用多GPU训练会悲剧的发现损失一直在下降、recall在上升,然而Obj几乎为零,最终得到的权重文件无法预测出bounding box。
使用多GPU训练前需要先用单GPU训练至Obj有稳定上升的趋势后(我一般在obj大于0.1后切换)再使用backup中备份的weights通过多GPU继续训练。一般情况下使用单GPU训练1000个迭代即可切换到多GPU。
./darknet detector train cfg/paul.data cfg/yolo-paul.cfg backup/yolo-paul_1000.weights -gpus 0,1,2,3,4,5,6,7 2>1 | tee paul_train_log.txt
0,1,2,3,4,5,6,7是指定的GPU的ID,通过
nvidia-smi
命令可以查询:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.20 Driver Version: 375.20 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla P100-SXM2... On | 0000:06:00.0 Off | 0 |
| N/A 52C P0 270W / 300W | 15887MiB / 16308MiB | 99% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla P100-SXM2... On | 0000:07:00.0 Off | 0 |
| N/A 55C P0 247W / 300W | 15887MiB / 16308MiB | 97% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla P100-SXM2... On | 0000:0A:00.0 Off | 0 |
| N/A 54C P0 252W / 300W | 15887MiB / 16308MiB | 98% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla P100-SXM2... On | 0000:0B:00.0 Off | 0 |
| N/A 51C P0 242W / 300W | 15887MiB / 16308MiB | 97% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla P100-SXM2... On | 0000:85:00.0 Off | 0 |
| N/A 53C P0 227W / 300W | 15887MiB / 16308MiB | 98% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla P100-SXM2... On | 0000:86:00.0 Off | 0 |
| N/A 58C P0 245W / 300W | 15887MiB / 16308MiB | 97% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla P100-SXM2... On | 0000:89:00.0 Off | 0 |
| N/A 59C P0 245W / 300W | 15887MiB / 16308MiB | 97% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla P100-SXM2... On | 0000:8A:00.0 Off | 0 |
| N/A 52C P0 228W / 300W | 15887MiB / 16308MiB | 97% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 50064 C ./darknet 15887MiB |
| 1 50064 C ./darknet 15887MiB |
| 2 50064 C ./darknet 15887MiB |
| 3 50064 C ./darknet 15887MiB |
| 4 50064 C ./darknet 15887MiB |
| 5 50064 C ./darknet 15887MiB |
| 6 50064 C ./darknet 15887MiB |
| 7 50064 C ./darknet 15887MiB |
+-----------------------------------------------------------------------------+
使用多GPU时的学习率
使用多GPU训练时,学习率是使用单GPU训练的n倍,n是使用GPU的个数
可视化训练过程的中间参数
等待训练结束后(有时候没等结束我们的模型就开始发散了),我们需要检查各项指标(如loss)是否达到了我们期望的数值,如果没有,要分析为什么。可视化训练过程的中间参数可以帮助我们分析问题。
可视化中间参数需要用到训练时保存的log文件paul_train_log.txt
训练log中各参数的意义
Region Avg IOU:平均的IOU,代表预测的bounding box和ground truth的交集与并集之比,期望该值趋近于1。
Class:是标注物体的概率,期望该值趋近于1.
Obj:期望该值趋近于1.
No Obj:期望该值越来越小但不为零.
Avg Recall:期望该值趋近1
avg:平均损失,期望该值趋近于0
使用train_loss_visualization.py脚本可以绘制loss变化曲线。
脚本已上传至GitHub(使用前需安装依赖):
https://github.com/PaulChongPeng/darknet/blob/master/tools/train_loss_visualization.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
lines =1878760
result = pd.read_csv('S:/Tools/Paul_YOLO/paul_train_log_new.txt', skiprows=[x for x in range(lines) if ((x%10!=9) |(x<1000))] ,error_bad_lines=False, names=['loss', 'avg', 'rate', 'seconds', 'images'])
result.head()
result['loss']=result['loss'].str.split(' ').str.get(1)
result['avg']=result['avg'].str.split(' ').str.get(1)
result['rate']=result['rate'].str.split(' ').str.get(1)
result['seconds']=result['seconds'].str.split(' ').str.get(1)
result['images']=result['images'].str.split(' ').str.get(1)
result.head()
result.tail()
#print(result.head())
# print(result.tail())
# print(result.dtypes)
print(result['loss'])
print(result['avg'])
print(result['rate'])
print(result['seconds'])
print(result['images'])
result['loss']=pd.to_numeric(result['loss'])
result['avg']=pd.to_numeric(result['avg'])
result['rate']=pd.to_numeric(result['rate'])
result['seconds']=pd.to_numeric(result['seconds'])
result['images']=pd.to_numeric(result['images'])
result.dtypes
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(result['avg'].values,label='avg_loss')
#ax.plot(result['loss'].values,label='loss')
ax.legend(loc='best')
ax.set_title('The loss curves')
ax.set_xlabel('batches')
fig.savefig('avg_loss')
#fig.savefig('loss')
脚本使用说明:
使用命令
2>1 | tee paul_train_log.txt
保存log时会生成两个文件,文件1里保存的是网络加载信息和checkout点保存信息,paul_train_log.txt中保存的是训练信息。
1、删除log开头的三行:
0,1,2,3,4,5,6,7
yolo-paul
Learning Rate: 1e-05, Momentum: 0.9, Decay: 0.0005
2、删除log的结尾几行,使最后一行为batch的输出,如:
497001: 0.863348, 0.863348 avg, 0.001200 rate, 5.422251 seconds, 107352216 images
3、执行extract_log.py脚本,格式化log。脚本代码见GitHub:
https://github.com/PaulChongPeng/darknet/blob/master/tools/extract_log.py
# coding=utf-8
# 该文件用来提取训练log,去除不可解析的log后使log文件格式化,生成新的log文件供可视化工具绘图
import random
f = open('paul_train_log.txt')
train_log = open('paul_train_log_new.txt', 'w')
for line in f:
# 去除多gpu的同步log
if 'Syncing' in line:
continue
# 去除除零错误的log
if 'nan' in line:
continue
train_log.write(line)
f.close()
train_log.close()
最终log格式:
Loaded: 5.588888 seconds
Region Avg IOU: 0.649881, Class: 0.854394, Obj: 0.476559, No Obj: 0.007302, Avg Recall: 0.737705, count: 61
Region Avg IOU: 0.671544, Class: 0.959081, Obj: 0.523326, No Obj: 0.006902, Avg Recall: 0.780000, count: 50
Region Avg IOU: 0.525841, Class: 0.815314, Obj: 0.449031, No Obj: 0.006602, Avg Recall: 0.484375, count: 64
Region Avg IOU: 0.583596, Class: 0.830763, Obj: 0.377681, No Obj: 0.007916, Avg Recall: 0.629214, count: 89
Region Avg IOU: 0.651377, Class: 0.908635, Obj: 0.460094, No Obj: 0.008060, Avg Recall: 0.753425, count: 73
Region Avg IOU: 0.571363, Class: 0.880554, Obj: 0.341659, No Obj: 0.007820, Avg Recall: 0.633663, count: 101
Region Avg IOU: 0.585424, Class: 0.935552, Obj: 0.358635, No Obj: 0.008192, Avg Recall: 0.644860, count: 107
Region Avg IOU: 0.599972, Class: 0.832793, Obj: 0.382910, No Obj: 0.009005, Avg Recall: 0.650602, count: 83
497001: 0.863348, 0.863348 avg, 0.000012 rate, 5.422251 seconds, 107352216 images
4、修改train_loss_visualization.py中lines为log行数,并根据需要修改要跳过的行数。
skiprows=[x for x in range(lines) if ((x%10!=9) |(x<1000))]
运行train_loss_visualization.py会在脚本所在路径生成avg_loss.png。
从损失变化曲线可以看出,模型在100000万次迭代后损失下降速度非常慢,几乎没有下降。结合log和cfg文件发现,我自定义的学习率变化策略在十万次迭代时会减小十倍,十万次迭代后学习率下降到非常小的程度,导致损失下降速度降低。修改cfg中的学习率变化策略,10万次迭代时不改变学习率,30万次时再降低。
我使用迭代97000次时的备份的checkout点来继续训练。
./darknet detector train cfg/paul.data cfg/yolo-paul.cfg backup/yolo-paul_97000.weights 2>1 | tee paul_train_log.txt
除了可视化loss,还可以可视化Avg IOU,Avg Recall等参数。
可视化’Region Avg IOU’, ‘Class’, ‘Obj’, ‘No Obj’, ‘Avg Recall’,’count’这些参数可以使用脚本train_iou_visualization.py,使用方式和train_loss_visualization.py相同。脚本已上传至GitHub:https://github.com/PaulChongPeng/darknet/blob/master/tools/train_iou_visualization.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
lines =525990
result = pd.read_csv('S:/Tools/Paul_YOLO/paul_train_log_new.txt', skiprows=[x for x in range(lines) if (x%10==0 or x%10==9) ] ,error_bad_lines=False, names=['Region Avg IOU', 'Class', 'Obj', 'No Obj', 'Avg Recall','count'])
result.head()
result['Region Avg IOU']=result['Region Avg IOU'].str.split(': ').str.get(1)
result['Class']=result['Class'].str.split(': ').str.get(1)
result['Obj']=result['Obj'].str.split(': ').str.get(1)
result['No Obj']=result['No Obj'].str.split(': ').str.get(1)
result['Avg Recall']=result['Avg Recall'].str.split(': ').str.get(1)
result['count']=result['count'].str.split(': ').str.get(1)
result.head()
result.tail()
#print(result.head())
# print(result.tail())
# print(result.dtypes)
print(result['Region Avg IOU'])
result['Region Avg IOU']=pd.to_numeric(result['Region Avg IOU'])
result['Class']=pd.to_numeric(result['Class'])
result['Obj']=pd.to_numeric(result['Obj'])
result['No Obj']=pd.to_numeric(result['No Obj'])
result['Avg Recall']=pd.to_numeric(result['Avg Recall'])
result['count']=pd.to_numeric(result['count'])
result.dtypes
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
#ax.plot(result['Region Avg IOU'].values,label='Region Avg IOU')
#ax.plot(result['Class'].values,label='Class')
#ax.plot(result['Obj'].values,label='Obj')
#ax.plot(result['No Obj'].values,label='No Obj')
ax.plot(result['Avg Recall'].values,label='Avg Recall')
#ax.plot(result['count'].values,label='count')
ax.legend(loc='best')
#ax.set_title('The Region Avg IOU curves')
ax.set_title('The Avg Recall curves')
ax.set_xlabel('batches')
#fig.savefig('Avg IOU')
fig.savefig('Avg Recall')
使用验证集评估模型
评估模型可以使用命令valid(只有预测结果,没有评价预测是否正确)或recall,这两个命令都无法满足我的需求,我实现了category命令做性能评估。
valid:
在paul.data末尾添加
eval = imagenet #有voc、coco、imagenet三种模式
修改Detector.c文件validate_detector函数,修改阈值(默认.005)
float thresh = .1;
重新编译然后执行命令
./darknet detector valid cfg/paul.data cfg/yolo-paul.cfg backup/yolo-paul_final.weights
results目录下会生成预测结果,格式如下:
1 1 0.431522 235.186066 77.746033 421.808258 348.950012
1 1 0.186538 161.324097 270.221497 187.429535 321.382141
1 14 0.166257 284.207947 364.423889 465.995056 454.305603
2 30 0.287718 274.455719 290.674194 343.506256 352.656433
2 30 0.582356 293.578918 294.799438 350.478088 327.216614
2 1 0.599921 138.686981 314.705231 352.362152 588.235962
3 59 0.251553 193.290497 183.707275 277.655273 349.782410
3 59 0.107120 209.172287 269.722626 330.998718 342.530914
3 62 0.162954 0.000000 278.525543 457.739563 480.000000
4 6 0.617184 38.155792 31.496445 434.091705 527.705811
4 1 0.101005 358.778351 238.540756 395.645050 289.902283
4 6 0.813770 75.790985 282.521210 459.018585 564.883545
4 3 0.114561 32.667072 407.288025 142.561798 506.885498
4 3 0.104120 87.489151 337.674896 446.883728 584.356689
5 1 0.106601 235.460571 0.707840 265.958740 34.851868
5 1 0.134753 310.776398 1.273307 344.392303 31.028347
5 1 0.146177 349.860596 0.445604 385.901550 29.931465
5 1 0.129790 388.831177 3.721551 419.852844 30.414955
5 1 0.146747 369.672150 0.000000 441.490387 45.012733
5 1 0.339233 7.567236 0.000000 53.692001 97.718735
如果想要查看recall可以使用recall命令。
修改费Detector.c文件的validate_detector_recall函数:
1、修改阈值:
float thresh = .25;
2、修改验证集路径:
list *plist = get_paths("/mnt/large4t/pengchong_data/Data/Paul/filelist/val.txt");
3、增加Precision
//fprintf(stderr, "%5d %5d %5dtRPs/Img: %.2ftIOU: %.2f%%tRecall:%.2f%%n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
fprintf(stderr, "ID:%5d Correct:%5d Total:%5dtRPs/Img: %.2ftIOU: %.2f%%tRecall:%.2f%%t", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
fprintf(stderr, "proposals:%5dtPrecision:%.2f%%n",proposals,100.*correct/(float)proposals);
重新编译然后执行命令
./darknet detector recall cfg/paul.data cfg/yolo-paul.cfg backup/yolo-paul_final.weights
结果格式如下:
ID: 0 Correct: 1 Total: 22 RPs/Img: 2.00 IOU: 7.59% Recall:4.55% proposals: 2 Precision:50.00%
ID: 1 Correct: 2 Total: 28 RPs/Img: 2.00 IOU: 8.90% Recall:7.14% proposals: 4 Precision:50.00%
ID: 2 Correct: 3 Total: 39 RPs/Img: 1.67 IOU: 7.91% Recall:7.69% proposals: 5 Precision:60.00%
ID: 3 Correct: 3 Total: 42 RPs/Img: 2.00 IOU: 7.42% Recall:7.14% proposals: 8 Precision:37.50%
ID: 4 Correct: 9 Total: 58 RPs/Img: 5.00 IOU: 15.96% Recall:15.52% proposals: 25 Precision:36.00%
ID: 5 Correct: 10 Total: 70 RPs/Img: 4.50 IOU: 14.99% Recall:14.29% proposals: 27 Precision:37.04%
ID: 6 Correct: 12 Total: 72 RPs/Img: 4.00 IOU: 16.51% Recall:16.67% proposals: 28 Precision:42.86%
ID: 7 Correct: 14 Total: 76 RPs/Img: 3.75 IOU: 17.60% Recall:18.42% proposals: 30 Precision:46.67%
ID: 8 Correct: 16 Total: 81 RPs/Img: 3.78 IOU: 19.15% Recall:19.75% proposals: 34 Precision:47.06%
ID: 9 Correct: 20 Total: 96 RPs/Img: 3.80 IOU: 20.40% Recall:20.83% proposals: 38 Precision:52.63%
ID: 10 Correct: 22 Total: 103 RPs/Img: 3.82 IOU: 21.09% Recall:21.36% proposals: 42 Precision:52.38%
category命令评估模型针对每种物体检测的性能
代码已提交至GitHub:https://github.com/PaulChongPeng/darknet/blob/master/src/detector.c
void print_category(FILE **fps, char *path, box *boxes, float **probs, int total, int classes, int w, int h, float thresh, float iou_thresh)
{
int i, j;
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;
box_label *truth = read_boxes(labelpath, &num_labels);
for(i = 0; i < total; ++i){
int class_id = max_index(probs[i],classes);
float prob = probs[i][class_id];
if (prob < thresh)continue;
float best_iou = 0;
int best_iou_id = 0;
int correct = 0;
for (j = 0; j < num_labels; ++j) {
box t = {truth[j].x*w, truth[j].y*h, truth[j].w*w, truth[j].h*h};
float iou = box_iou(boxes[i], t);
//fprintf(stderr, "box p: %f, %f, %f, %fn", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h);
//fprintf(stderr, "box t: %f, %f, %f, %fn", t.x, t.y, t.w, t.h);
//fprintf(stderr, "iou : %fn", iou);
if(iou > best_iou){
best_iou = iou;
best_iou_id = j;
}
}
if(best_iou > iou_thresh && truth[best_iou_id].id == class_id){
correct = 1;
}
float xmin = boxes[i].x - boxes[i].w/2.;
float xmax = boxes[i].x + boxes[i].w/2.;
float ymin = boxes[i].y - boxes[i].h/2.;
float ymax = boxes[i].y + boxes[i].h/2.;
if (xmin < 0) xmin = 0;
if (ymin < 0) ymin = 0;
if (xmax > w) xmax = w;
if (ymax > h) ymax = h;
fprintf(fps[class_id], "%s, %d, %d, %f, %f, %f, %f, %f, %fn", path, class_id, correct, prob, best_iou, xmin, ymin, xmax, ymax);
}
}
void validate_detector_category(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 = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %gn", 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];
FILE **fps = 0;
if(!outfile) outfile = "paul_";
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");
}
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
int t;
float thresh = .25;
float iou_thresh = .5;
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;
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);
}
time_t start = time(0);
for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%dn", 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];
float *X = val_resized[t].data;
network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
get_region_boxes(l, w, h, thresh, probs, boxes, 0, map, .5);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
print_category(fps, path, boxes, probs, l.w*l.h*l.n, classes, w, h, thresh, iou_thresh);
free_image(val[t]);
free_image(val_resized[t]);
}
}
for(j = 0; j < classes; ++j){
if(fps) fclose(fps[j]);
}
fprintf(stderr, "Total Detection Time: %f Secondsn", (double)(time(0) - start));
}
void run_detector(int argc, char **argv)
{
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .24);
float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
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);
char *outfile = find_char_arg(argc, argv, "-out", 0);
int *gpus = 0;
int gpu = 0;
int ngpus = 0;
if(gpu_list){
printf("%sn", 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");
char *datacfg = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh);
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], "recall")) validate_detector_recall(cfg, weights);
else if(0==strcmp(argv[2], "category"))validate_detector_category(datacfg, cfg, weights, outfile);
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, hier_thresh);
}
}
执行命令
./darknet detector category cfg/paul.data cfg/yolo-paul.cfg backup/yolo-paul_final.weights
result目录下会生成各类物体的val结果,有多少种物体,就会生成多少个txt文件,每个txt文件中有path, class_id, correct, prob, best_iou, xmin, ymin, xmax, ymax信息。
使用evalute.py工具可以解析这些txt文件做一个总结性的评估。
工具已上传到GitHub:https://github.com/PaulChongPeng/darknet/blob/master/tools/evalute.py
# coding=utf-8
# 本工具和category命令结合使用
# category是在detector.c中新增的命令,主要作用是生成每类物体的evalute结果
# 执行命令 ./darknet detector category cfg/paul.data cfg/yolo-paul.cfg backup/yolo-paul_final.weights
# result目录下会生成各类物体的val结果,将本工具放在result目录下执行,会print出各种物体的evalute结果,包括
# id,avg_iou,avg_correct_iou,avg_precision,avg_recall,avg_score
# result目录下会生成low_list和high_list,内容分别为精度和recall未达标和达标的物体种类
import os
from os import listdir, getcwd
from os.path import join
import shutil
# 共有多少类物体
class_num = 97
# 每类物体的验证结果
class CategoryValidation:
id = 0 # Category id
path = "" # path
total_num = 0 # 标注文件中该类bounding box的总数
proposals_num = 0 # validate结果中共预测了多少个该类的bounding box
correct_num = 0 # 预测正确的bounding box(与Ground-truth的IOU大于0.5且种类正确)的数量
iou_num = 0 # 所有大于0.5的IOU的数量
iou_sum = 0 # 所有大于0.5的IOU的IOU之和
correct_iou_sum = 0 # 预测正确的bounding box的IOU之和
score_sum = 0 # 所有正确预测的bounding box的概率之和
avg_iou = 0 # 无论预测的bounding box的object的种类是否正确,所有bounding box 与最吻合的Ground-truth求出IOU,对大于0.5的IOU求平均值:avg_iou = iou_sum/iou_num
avg_correct_iou = 0 # 对预测正确的bounding box的IOU求平均值:avg_correct_iou = correct_iou_sum/correct_num
avg_precision = 0 # avg_precision = correct_num/proposals_num
avg_recall = 0 # avg_recall = correct_num/total_num
avg_score = 0 # avg_score=score_sum/correct_num
def __init__(self, path, val_cat_num):
self.path = path
f = open(path)
for line in f:
temp = line.rstrip().replace(' ', '').split(',', 9)
temp[1] = int(temp[1])
self.id = temp[1]
self.total_num = val_cat_num[self.id]
if (self.total_num):
break
for line in f:
# path, class_id, correct, prob, best_iou, xmin, ymin, xmax, ymax
temp = line.rstrip().split(', ', 9)
temp[1] = int(temp[1])
temp[2] = int(temp[2])
temp[3] = float(temp[3])
temp[4] = float(temp[4])
self.proposals_num = self.proposals_num + 1.00
if (temp[2]):
self.correct_num = self.correct_num + 1.00
self.score_sum = self.score_sum + temp[3]
self.correct_iou_sum = self.correct_iou_sum + temp[4]
if (temp[4] > 0.5):
self.iou_num = self.iou_num + 1
self.iou_sum = self.iou_sum + temp[4]
self.avg_iou = self.iou_sum / self.iou_num
self.avg_correct_iou = self.correct_iou_sum / self.correct_num
self.avg_precision = self.correct_num / self.proposals_num
self.avg_recall = self.correct_num / self.total_num
self.avg_score = self.score_sum / self.correct_num
f.close()
# 导出识别正确的图片列表
def get_correct_list(self):
f = open(self.path)
new_f_name = "correct_list_" + self.id + ".txt"
new_f = open(new_f_name, 'w')
for line in f:
temp = line.rstrip().split(', ', 9)
if (temp[2]):
new_f.write(line)
f.close()
# 导出识别错误的图片列表
def get_error_list(self):
f = open(self.path)
new_f_name = "error_list_" + self.id + ".txt"
new_f = open(new_f_name, 'w')
for line in f:
temp = line.rstrip().split(', ', 9)
if (temp[2] == 0):
new_f.write(line)
f.close()
def print_eva(self):
print("id=%d, avg_iou=%f, avg_correct_iou=%f, avg_precision=%f, avg_recall=%f, avg_score=%f n" % (self.id,
self.avg_iou,
self.avg_correct_iou,
self.avg_precision,
self.avg_recall,
self.avg_score))
def IsSubString(SubStrList, Str):
flag = True
for substr in SubStrList:
if not (substr in Str):
flag = False
return flag
# 获取FindPath路径下指定格式(FlagStr)的文件名列表
def GetFileList(FindPath, FlagStr=[]):
import os
FileList = []
FileNames = os.listdir(FindPath)
if (len(FileNames) > 0):
for fn in FileNames:
if (len(FlagStr) > 0):
if (IsSubString(FlagStr, fn)):
FileList.append(fn)
else:
FileList.append(fn)
if (len(FileList) > 0):
FileList.sort()
return FileList
# 获取所有物体种类的ROI数目
# path是图片列表的地址
# 返回值是一个list,list的索引是物体种类在yolo中的id,值是该种物体的ROI数量
def get_val_cat_num(path):
val_cat_num = []
for i in range(0, class_num):
val_cat_num.append(0)
f = open(path)
for line in f:
label_path = line.rstrip().replace('images', 'labels')
label_path = label_path.replace('JPEGImages', 'labels')
label_path = label_path.replace('.jpg', '.txt')
label_path = label_path.replace('.JPEG', '.txt')
label_list = open(label_path)
for label in label_list:
temp = label.rstrip().split(" ", 4)
id = int(temp[0])
val_cat_num[id] = val_cat_num[id] + 1.00
label_list.close()
f.close()
return val_cat_num
# 获取物体名list
# path是物体名list文件地址
# 返回值是一个列表,列表的索引是类的id,值为该类物体的名字
def get_name_list(path):
name_list = []
f = open(path)
for line in f:
temp = line.rstrip().split(',', 2)
name_list.append(temp[1])
return name_list
wd = getcwd()
val_result_list = GetFileList(wd, ['txt'])
val_cat_num = get_val_cat_num("/raid/pengchong_data/Data/filelists/val.txt")
name_list = get_name_list("/raid/pengchong_data/Tools/Paul_YOLO/data/paul_list.txt")
low_list = open("low_list.log", 'w')
high_list = open("high_list.log", 'w')
for result in val_result_list:
cat = CategoryValidation(result, val_cat_num)
cat.print_eva()
if ((cat.avg_precision < 0.3) | (cat.avg_recall < 0.3)):
low_list.write("id=%d, name=%s, avg_precision=%f, avg_recall=%f n" % (cat.id, name_list[cat.id], cat.avg_precision, cat.avg_recall))
if ((cat.avg_precision > 0.6) & (cat.avg_recall > 0.6)):
high_list.write("id=%d, name=%s, avg_precision=%f, avg_recall=%f n" % (cat.id, name_list[cat.id], cat.avg_precision, cat.avg_recall))
low_list.close()
high_list.close()
将本工具放在result目录下执行,会print出各种物体的evalute结果,包括
id,avg_iou,avg_correct_iou,avg_precision,avg_recall,avg_score。
id=1, avg_iou=0.807394, avg_correct_iou=0.810435, avg_precision=0.473983, avg_recall=0.283531, avg_score=0.661014
id=2, avg_iou=0.824890, avg_correct_iou=0.826227, avg_precision=0.812950, avg_recall=0.824818, avg_score=0.772828
id=3, avg_iou=0.748561, avg_correct_iou=0.756006, avg_precision=0.401891, avg_recall=0.146048, avg_score=0.568196
id=4, avg_iou=0.821225, avg_correct_iou=0.822419, avg_precision=0.779621, avg_recall=0.798544, avg_score=0.773700
id=5, avg_iou=0.722905, avg_correct_iou=0.721078, avg_precision=0.391119, avg_recall=0.255361, avg_score=0.552248
id=6, avg_iou=0.814797, avg_correct_iou=0.814427, avg_precision=0.731707, avg_recall=0.612245, avg_score=0.833531
id=7, avg_iou=0.713375, avg_correct_iou=0.702796, avg_precision=0.739336, avg_recall=0.715596, avg_score=0.691065
id=8, avg_iou=0.785120, avg_correct_iou=0.797686, avg_precision=0.582267, avg_recall=0.594216, avg_score=0.734099
id=9, avg_iou=0.744355, avg_correct_iou=0.752729, avg_precision=0.523982, avg_recall=0.241049, avg_score=0.650683
id=10, avg_iou=0.736755, avg_correct_iou=0.744951, avg_precision=0.621368, avg_recall=0.382028, avg_score=0.651450
同时result目录下会生成low_list和high_list,内容分别为精度和recall未达标和达标的物体种类。
最后
以上就是霸气老师为你收集整理的YOLO训练准备训练数据准备配置文件训练多GPU训练技巧可视化训练过程的中间参数使用验证集评估模型的全部内容,希望文章能够帮你解决YOLO训练准备训练数据准备配置文件训练多GPU训练技巧可视化训练过程的中间参数使用验证集评估模型所遇到的程序开发问题。
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