【YOLOv3】基于darknet的训练过程(简洁版)

文章目录

  • 准备工作
  • 训练自己的数据集
  • 开始训练
  • 训练日志可视化

准备工作

参考链接:https://pjreddie.com/darknet/yolo/

  1. 下载darknet
  2. 修改Makefile
  3. 下载yolov3.weights
  4. 下载darknet53.conv.74(后面训练数据集会用到,先下载好了,留着备用)
  5. 测试
#1. 下载`darknet`
git clone https://github.com/pjreddie/darknet

#2. 修改`Makefile`
cd darknet
make

#3. 下载`yolov3.weights`
wget https://pjreddie.com/media/files/yolov3.weights

#4. 下载`darknet53.conv.74`
wget https://pjreddie.com/media/files/darknet53.conv.74

#5. 测试图片
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/dog.jpg

#5. 测试视频
./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights <video file>

#5. 加载摄像头
./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights

总结:可以将上述下载的文件备份一下,以后每次自己训练数据集的时候就不用再次下载了,直接改用。

训练自己的数据集

参考链接:https://www.cnblogs.com/answerThe/p/11481564.html

  1. 在darknet文件夹下新建一个myData文件夹。将标注好的图片和xml文件放到对应目录下。运行test.py生成train.txt/val.txt/test.txt/trainval.txt文件。myData包含如下文件(夹):
myData
  ......JPEGImages           #存放图像
  ......Annotations          #存放图像对应的xml文件
  ......ImageSets/Main       #存放训练/存放train.txt/val.txt/test.txt/trainval.txt文件
  ......test.py              #生成train.txt/val.txt/test.txt/trainval.txt文件

test.py代码如下:

import os
import random

trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = 'Annotations'
txtsavepath = 'ImageSets\Main'
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)

ftrainval = open('ImageSets/Main/trainval.txt', 'w')
ftest = open('ImageSets/Main/test.txt', 'w')
ftrain = open('ImageSets/Main/train.txt', 'w')
fval = open('ImageSets/Main/val.txt', 'w')

for i in list:
    name = total_xml[i][:-4] + '\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftest.write(name)
        else:
            fval.write(name)
    else:
        ftrain.write(name)

ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

如果按照上述文件结构,则test.py文件不需要修改,直接运行,即可生成txt文件。

  1. 将darknet文件夹下的scripts/voc_label.py拷贝出来,修改成my_labels.py放在darknet文件夹中。【注意修改类别和路径】

运行该脚本my_lables.py会在./myData目录下生成一个labels文件夹一个txt文件(myData_train.txt)(内容是: 类别的编码和目标的相对位置)。

my_labels.py代码如下:

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
 
sets=[('myData', 'train'), ('myData', 'val'), ('myData', 'train'), ('myData', 'val'), ('myData', 'test')]

classes = ["person", "foot", "face"]                     # 改成自己的类别
 
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('myData/Annotations/%s.xml'%(image_id))
    out_file = open('myData/labels/%s.txt'%(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('myData/labels/'):     # 改成自己建立的myData
        os.makedirs('myData/labels/')
    image_ids = open('myData/ImageSets/Main/%s.txt'%(image_set)).read().strip().split()
    list_file = open('myData/%s_%s.txt'%(year, image_set), 'w')
    for image_id in image_ids:
        list_file.write('%s/myData/JPEGImages/%s.jpg\n'%(wd, image_id))
        convert_annotation(year, image_id)
    list_file.close()

注意:这里面如果采用上述文件结构,只需要将classes改成自己的类别即可,其他内容不需要修改。

  1. myData文件夹下新建myData.names文件。
    【YOLOv3】基于darknet的训练过程(简洁版)_第1张图片
  2. myData文件夹下新建weights文件,用于保存生成的权重文件。
  3. 修改darknet/cfg下的voc.datayolov3-voc.cfg文件。
    复制这两个文件,并分别重命名为my_data.datamy_yolov3.cfg

(1)修改my_data.data

classes= 4                                            #改为自己的分类个数

##下面都改为自己的路径
train  = /home/zhan/darknet/myData/myData_train.txt  
valid  =/home/zhan/darknet/myData/myData_test.txt
names = /home/zhan/darknet/myData/myData.names 
backup = /home/zhan/darknet/myData/weights

(2)修改my_yolov3.cfg

Ctrl+F,搜出3个含有yolo的地方。每个地方都必须要改2处,filters 、classes
filters:3*(5+len(classes))
可修改:random = 1:原来是1,显存小改为0。(是否要多尺度输出。)

一般地,max_batches修改成合适的数值。

开始训练

参考链接:https://blog.csdn.net/csdn_zhishui/article/details/85397380

  1. 训练
    首先,将cfg/my_yolov3.cfg文件中改成Training模式。
    【YOLOv3】基于darknet的训练过程(简洁版)_第2张图片
    训练命令:
./darknet detector train cfg/my_data.data cfg/my_yolov3.cfg darknet53.conv.74

# 指定gpu训练,默认使用gpu0(查看GPU情况,`nvidia-smi`)
./darknet detector train cfg/my_data.data cfg/my_yolov3.cfg darknet53.conv.74 -gups 0,1,2,3

# 训练过程中保存训练日志xxx.log
./darknet detector train cfg/my_data.data cfg/my_yolov3.cfg darknet53.conv.74 | tee train_yolov3.log 

# 断点继续训练
./darknet detector train cfg/my_data.data cfg/my_yolov3.cfg myData/weights/my_yolov3.backup | tee new_train_yolov3.log 

训练日志可视化

vis_yolov3_log.py代码如下:

# -*- coding: utf-8 -*-
import pandas as pd
import matplotlib.pyplot as plt
import os

# ==================可能需要修改的地方=====================================#
g_log_path = "train_yolov3.log"     # 此处修改为自己的训练日志文件名
# ==========================================================================#

def extract_log(log_file, new_log_file, key_word):
    '''
    :param log_file:日志文件
    :param new_log_file:挑选出可用信息的日志文件
    :param key_word:根据关键词提取日志信息
    :return:
    '''
    with open(log_file, "r") as f:
        with open(new_log_file, "w") as train_log:
            for line in f:
                # 去除多gpu的同步log
                if "Syncing" in line:
                    continue
                # 去除nan log
                if "nan" in line:
                    continue
                if key_word in line:
                    train_log.write(line)
    f.close()
    train_log.close()

def drawAvgLoss(loss_log_path):
    '''
    :param loss_log_path: 提取到的loss日志信息文件
    :return: 画loss曲线图
    '''
    line_cnt = 0
    for count, line in enumerate(open(loss_log_path, "rU")):
        line_cnt += 1
    result = pd.read_csv(loss_log_path, skiprows=[iter_num for iter_num in range(line_cnt) if ((iter_num < 500))],
                         error_bad_lines=False,
                         names=["loss", "avg", "rate", "seconds", "images"])
    result["avg"] = result["avg"].str.split(" ").str.get(1)
    result["avg"] = pd.to_numeric(result["avg"])

    fig = plt.figure(1, figsize=(6, 4))
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(result["avg"].values, label="Avg Loss", color="#ff7043")
    ax.legend(loc="best")
    ax.set_title("Avg Loss Curve")
    ax.set_xlabel("Batches")
    ax.set_ylabel("Avg Loss")

def drawIOU(iou_log_path):
    '''
    :param iou_log_path: 提取到的iou日志信息文件
    :return: 画iou曲线图
    '''
    line_cnt = 0
    for count, line in enumerate(open(iou_log_path, "rU")):
        line_cnt += 1
    result = pd.read_csv(iou_log_path, skiprows=[x for x in range(line_cnt) if (x % 39 != 0 | (x < 5000))],
                         error_bad_lines=False,
                         names=["Region Avg IOU", "Class", "Obj", "No Obj", "Avg Recall", "count"])
    result["Region Avg IOU"] = result["Region Avg IOU"].str.split(": ").str.get(1)

    result["Region Avg IOU"] = pd.to_numeric(result["Region Avg IOU"])

    result_iou = result["Region Avg IOU"].values
    # 平滑iou曲线
    for i in range(len(result_iou) - 1):
        iou = result_iou[i]
        iou_next = result_iou[i + 1]
        if abs(iou - iou_next) > 0.2:
            result_iou[i] = (iou + iou_next) / 2

    fig = plt.figure(2, figsize=(6, 4))
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(result_iou, label="Region Avg IOU", color="#ff7043")
    ax.legend(loc="best")
    ax.set_title("Avg IOU Curve")
    ax.set_xlabel("Batches")
    ax.set_ylabel("Avg IOU")

if __name__ == "__main__":
    loss_log_path = "train_log_loss.txt"
    iou_log_path = "train_log_iou.txt"
    if os.path.exists(g_log_path) is False:
        exit(-1)
    if os.path.exists(loss_log_path) is False:
        extract_log(g_log_path, loss_log_path, "images")
    if os.path.exists(iou_log_path) is False:
        extract_log(g_log_path, iou_log_path, "IOU")
    drawAvgLoss(loss_log_path)
    drawIOU(iou_log_path)
    plt.show()

可视化这部分除了需要将训练日志文件名修改成自己的,还要特别注意skiprows=[iter_num for iter_num in range(line_cnt) if ((iter_num < 500))]skiprows=[x for x in range(line_cnt) if (x % 39 != 0 | (x < 5000))]这两部分,需要根据自己的训练次数来设定的。
分别表示,迭代次数小于500次的跳过,画图不用,从501开始画图;每隔39个数或者前5000个数跳过,说白了就是,前5000个数值舍弃,从第5001个数开始,每隔39个数取一个数值参与画图。

附录两张非常令人糟心的图(因为不知怎么地,它就失败了~~)
【YOLOv3】基于darknet的训练过程(简洁版)_第3张图片【YOLOv3】基于darknet的训练过程(简洁版)_第4张图片

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