YOLOV5-4.0训练自己的数据集

￡神魔★判官ぃ 2022-11-17 16:09 264阅读 0赞

# 个人公众号 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob25nbG9uZ3NoZW4_size_16_color_FFFFFF_t_70]

# 环境准备 #

## python环境 ##

python>=3.8.0

## pytorch环境 ##

pip install torch==1.7.0+cu101 torchvision==0.8.1+cu101 torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

检查是否安装成功

import torch
    torch.cuda.is_available()

# 下载源码以及安装依赖 #

pip install -r requirements.txt

# 数据标注以及数据准备 #

## 数据标注 ##

这里主要利用labelme软件进行数据标注，关于该软件的使用，可以参考前面的文章；最后我们将图像放在data/images目录下，xml文件放在data/Annotations目录下。

## 数据预处理 ##

### 划分训练集和测试集 ###

import os
    import random
    import argparse
    
    parser = argparse.ArgumentParser()
    parser.add_argument('--xml_path', default='data/Annotations', type=str, help='input xml label path')
    parser.add_argument('--txt_path', default='data/labels', type=str, help='output txt label path')
    opt = parser.parse_args()
    
    trainval_percent = 1.0
    train_percent = 0.9
    xmlfilepath = opt.xml_path
    txtsavepath = opt.txt_path
    total_xml = os.listdir(xmlfilepath)
    if not os.path.exists(txtsavepath):
        os.makedirs(txtsavepath)
    
    num = len(total_xml)
    list_index = range(num)
    tv = int(num * trainval_percent)
    tr = int(tv * train_percent)
    trainval = random.sample(list_index, tv)
    train = random.sample(trainval, tr)
    
    file_trainval = open(txtsavepath + '/trainval.txt', 'w')
    file_test = open(txtsavepath + '/test.txt', 'w')
    file_train = open(txtsavepath + '/train.txt', 'w')
    file_val = open(txtsavepath + '/val.txt', 'w')
    
    for i in list_index:
        name = total_xml[i][:-4] + '\n'
        if i in trainval:
            file_trainval.write(name)
            if i in train:
                file_train.write(name)
            else:
                file_val.write(name)
        else:
            file_test.write(name)
    
    file_trainval.close()
    file_train.close()
    file_val.close()
    file_test.close()

### 将数据集转化为yolo数据格式 ###

# -*- coding: utf-8 -*-
    
    import xml.etree.ElementTree as ET
    from tqdm import tqdm
    import os
    from os import getcwd
    
    sets = ['train', 'val', 'test']
    classes = ['Helmet','person']
    
    
    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(image_id):
        # try:
            in_file = open('VOCData/Annotations/%s.xml' % (image_id), encoding='utf-8')
            out_file = open('VOCData/labels/%s.txt' % (image_id), 'w', encoding='utf-8')
            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))
                b1, b2, b3, b4 = b
                # 标注越界修正
                if b2 > w:
                    b2 = w
                if b4 > h:
                    b4 = h
                b = (b1, b2, b3, b4)
                bb = convert((w, h), b)
                out_file.write(str(cls_id) + " " +
                               " ".join([str(a) for a in bb]) + '\n')
        # except Exception as e:
        #     print(e, image_id)
    
    
    wd = getcwd()
    for image_set in sets:
        if not os.path.exists('VOCData/labels/'):
            os.makedirs('VOCData/labels/')
        image_ids = open('VOCData/labels/%s.txt' %
                         (image_set)).read().strip().split()
        list_file = open('VOCData/%s.txt' % (image_set), 'w')
        for image_id in tqdm(image_ids):
            list_file.write('VOCData/images/%s.jpg\n' % (image_id))
            convert_annotation(image_id)
        list_file.close()

代码运行成功后，在data/labels下生成每个图的txt文件。

### 在data文件夹下创建helmet.yaml文件,并根据自己的具体情况修改如下： ###

# download command/URL (optional)
    download: bash data/scripts/get_voc.sh
    
    # 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  # 16551 images
    val: data/val.txt  # 4952 images
    
    # number of classes
    nc: 2
    
    # class names
    names: [ 'helmet','person' ]

# 模型训练 #

## 预训练模型下载 ##

这里我选择的是系列里面最小的模型，yolo5s.pt

## 开始训练 ##

修改models/yolov5s.yaml的类别数：

# parameters
    nc: 2  # number of classes
    depth_multiple: 0.33  # model depth multiple
    width_multiple: 0.50  # layer channel multiple

### 训练命令行 ###

python train.py --img 640 --batch 4 --epoch 300 --data ./data/helmet.yaml --cfg ./models/yolov5s.yaml --weights weights/yolov5s.pt --workers 0

# 参考 #

*  https://github.com/ultralytics/yolov5/releases/tag/v4.0
 *

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob25nbG9uZ3NoZW4_size_16_color_FFFFFF_t_70]: /images/20221022/296903ca5aac4b14b9d6d85b5b8444d5.png