pytorch—yolov3训练自己的数据集

Bertha 。 2023-02-23 05:15 45阅读 0赞

注意：本篇博客直接使用VOC2007数据集

1.数据集

Labelimg软件构建数据集，Labelimg项目地址：https://github.com/tzutalin/labelImg，Labelimg快捷键：

Ctrl + u    Load all of the images from a directory  
Ctrl + r    Change the default annotation target dir  
Ctrl + s    Save  
Ctrl + d    Copy the current label and rect box  
Space    Flag the current image as verified  
w    Create a rect box  
d    Next image  
a    Previous image  
del    Delete the selected rect box  
Ctrl++    Zoom in  
Ctrl--    Zoom out  
↑→↓←    Keyboard arrows to move selected rect box  
voc2007数据集目录结构 ：

----voc2007

----Annotations

----ImageSets

----Main

----JPEGImages

在根目录下新建makeTXT.py，将数据集划分，并且在Main文件夹下构建4个TXT：train.txt，test.txt，trainval.txt，val.txt。代码如下：

import os
    import random
    
    trainval_percent = 0.1
    train_percent = 0.9
    xmlfilepath = 'data/Annotations'
    txtsavepath = 'data/ImageSets'
    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('data/ImageSets/trainval.txt', 'w')
    ftest = open('data/ImageSets/test.txt', 'w')
    ftrain = open('data/ImageSets/train.txt', 'w')
    fval = open('data/ImageSets/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()

在 根目录下新建voclabel.py，生成labels。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 = ['train', 'test','val']
    classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
    
    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)
    def convert_annotation(image_id):
        in_file = open('data/Annotations/%s.xml' % (image_id))
        out_file = open('data/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()
    print(wd)
    for image_set in sets:
        if not os.path.exists('data/labels/'):
            os.makedirs('data/labels/')
        image_ids = open('data/ImageSets/%s.txt' % (image_set)).read().strip().split()
        list_file = open('data/%s.txt' % (image_set), 'w')
        for image_id in image_ids:
            list_file.write('data/images/%s.jpg\n' % (image_id))
            convert_annotation(image_id)
        list_file.close()

数据格式如下图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxeW91aGFwcHk_size_16_color_FFFFFF_t_70][]

其中images里面存放的是JPEGImages的全部图片。

2.环境

（1）git clone https://github.com/ultralytics/yolov3.git

（2）pip install -U -r requirements.txt

（5）在项目根目录下新建weights文件夹，下载权重文件，将其放入weights文件夹中。

（6）测试：./darknet detect cfg/yolov3.cfg weights/yolov3.pt  data/samples/bus.jpg    或     ./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.pt  data/samples/

3.训练模型

（1）下载。

（2）在data目录下新建\*\*.name文件，存放你的数据集类别名称。本文用coco.names:

aeroplane
    bicycle
    bird
    boat
    bottle
    bus
    car
    cat
    chair
    cow
    diningtable
    dog
    horse
    motorbike
    person
    pottedplant
    sheep
    sofa
    train
    tvmonitor

（3）在data目录下新建\*\*.data文件，本文用coco.data：

classes = 20\#类别数  
train = data\\2007\_train.txt\#voc\_labels.py生成的训练集的位置  
valid = data\\2007\_test.txt  
names = data\\coco.names  
backup = backup\\  
（4） 更新cfg文件的classes，本文使用的classes=20。yolo上一卷积层的filters=3\*（classes+5），其中5代表的是4个坐标+1个置信度。

（5）开始训练：python train.py --data data/coco.data --cfg cfg/yolov3.cfg  --weights weights/yolov3.pt

中断后，恢复训练：python train.py --data data/coco.data --cfg cfg/yolov3.cfg  --weights weights/yolov3.pt  --resume

注意：max\_batches = 50200 \#\#\# 迭代次数

（6）测试：

python detect.py --cfg cfg/yolov3.cfg --weights/\*\*\*\*\*\*.pt --source data/samples/file.jpg

*  Image: `--source file.jpg`
 *  Video: `--source file.mp4`

（7）评估模型：

python test.py --data  data/coco.data  --cfg cfg/yolov3.cfg --weights weights/\*\*\*\*\*\*.pt

（8）可视化：

Python -c from utils import utils; utils.plot\_results()

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