yolov5训练自己的VOC数据集

深藏阁楼爱情的钟 2022-12-25 11:59 273阅读 0赞

一.VOC数据准备

![20201201204327773.png][]

voc数据集目录结构 ：

----voc

----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 = 'voc/Annotations'
    txtsavepath = 'voc/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('voc/ImageSets/Main/trainval.txt', 'w')
    ftest = open('voc/ImageSets/Main/test.txt', 'w')
    ftrain = open('voc/ImageSets/Main/train.txt', 'w')
    fval = open('data/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()

在根目录下新建voc\_label.py，生成labels文件夹，及用于yolov5训练的train.txt，text.txt，val.txt。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"]
     
    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()

用于训练的数据集如下图：

![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。

二.环境搭建

（1）git clone https://github.com/ultralytics/yolov5.git

（2）pip install -U -r requirements.txt

（3）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。

三.训练自己的模型

（1）在data文件夹里面新建test.yaml，

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
    train: data/train.txt  # voclabel.py生成的train.txt的路径
    val: data/train.txt   # voclabel.py生成的val.txt的路径
    
    # number of classes
    nc: 1
    
    # class names
    names: ['person']

（2）在models文件夹里面找到自己需要的<model>.yaml,修改nc为自己的类别数：

# parameters
    nc: 1  # number of classes
    depth_multiple: 0.33  # model depth multiple
    width_multiple: 0.50  # layer channel multiple
    
    # anchors
    anchors:
      - [10,13, 16,30, 33,23]  # P3/8
      - [30,61, 62,45, 59,119]  # P4/16
      - [116,90, 156,198, 373,326]  # P5/32
    
    # YOLOv5 backbone
    backbone:
      # [from, number, module, args]
      [[-1, 1, Focus, [64, 3]],  # 0-P1/2
       [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
       [-1, 3, BottleneckCSP, [128]],
       [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
       [-1, 9, BottleneckCSP, [256]],
       [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
       [-1, 9, BottleneckCSP, [512]],
       [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
       [-1, 1, SPP, [1024, [5, 9, 13]]],
       [-1, 3, BottleneckCSP, [1024, False]],  # 9
      ]
    
    # YOLOv5 head
    head:
      [[-1, 1, Conv, [512, 1, 1]],
       [-1, 1, nn.Upsample, [None, 2, 'nearest']],
       [[-1, 6], 1, Concat, [1]],  # cat backbone P4
       [-1, 3, BottleneckCSP, [512, False]],  # 13
    
       [-1, 1, Conv, [256, 1, 1]],
       [-1, 1, nn.Upsample, [None, 2, 'nearest']],
       [[-1, 4], 1, Concat, [1]],  # cat backbone P3
       [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
    
       [-1, 1, Conv, [256, 3, 2]],
       [[-1, 14], 1, Concat, [1]],  # cat head P4
       [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
    
       [-1, 1, Conv, [512, 3, 2]],
       [[-1, 10], 1, Concat, [1]],  # cat head P5
       [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)
    
       [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
      ]

（3）训练模型：python train.py --data data/test.yaml --cfg models/<model>.yaml  --weights weights/yolov5s.pt --device 0

device=0表示用第1个GPU训练

中断后，恢复训练：python train.py --data data/test.yaml --cfg models/<model>.yaml  --weights weights/yolov5s.pt --device 0  --resume

**注意：**

官方的训练：

python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                             yolov5m                                40
                                             yolov5l                                24
                                             yolov5x                                16

（4）训练之后，权重会保存在 `./runs` 文件夹里面的每个 `exp` 文件里面的 `weights文件夹下。`

（5）推理：图片结果会保存在 `./inferenct/output/` 文件夹下：python detect.py --source 图片路径 --weights ./weights/（自己训练的模型）.pt

`python detect.py --source 0 # webcam```

`            file.jpg # image`

`           ``file.mp4 # video```

`             path/ # directory```

`          path/*.jpg # glob`

四：生成ONNX

（1）pip install onnx

（2）python ./models/export.py --weights ./weights/（自己训练的模型）.pt --img 640 --batch 4

[20201201204327773.png]: /images/20221120/4eb1147196d644c58de55f22f3ebe137.png
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