yolov7训练数据集详细流程bike-car-person

一、准备深度学习环境

下载yolov7代码

下载完成解压放在自己的主目录

命名yolov7-4

二、 准备自己的数据集

1.进入主目录

2.进入data目录下把你的xml文件夹命名为Annotations，把你的存放图片文件夹命名为images

3.分别新建ImageSets、imagtest（里面存放测试图片）、labels（里面存放转换之后的yolo格式文件）

三、 1.2.在data目录下新建split_train_val.py文件

里面内容如下

# coding:utf-8
 
import os
import random
import argparse
 
parser = argparse.ArgumentParser()
#xml文件的地址，根据自己的数据进行修改 xml一般存放在Annotations下
parser.add_argument('--xml_path', default='Annotations', type=str, help='input xml label path')
#数据集的划分，地址选择自己数据下的ImageSets/Main
parser.add_argument('--txt_path', default='ImageSets/Main', 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()

运行之后会在ImageSets/Main下生成四个.txt文件

2.在data目录下新建voc_label.py文件，里面存放代码，里面classes需要改成自己的类别

# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import os
from os import getcwd
 
sets = ['train', 'val', 'test']
classes = ['bike','carsgraz','person']   # 改成自己的类别
abs_path = os.getcwd()
print(abs_path)
 
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):
    in_file = open('./Annotations/%s.xml' % (image_id), encoding='UTF-8')
    out_file = open('./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))
        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')
 
wd = getcwd()
for image_set in sets:
    if not os.path.exists('./labels/'):
        os.makedirs('./labels/')
    image_ids = open('./ImageSets/Main/%s.txt' % (image_set)).read().strip().split()
    list_file = open('./%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        list_file.write(abs_path + '/images/%s.png\n' % (image_id)) # 注意你的图片格式，如果是.jpg记得修改
        convert_annotation(image_id)
    list_file.close()

3.拷贝一份coco.yaml文件里面改成自己的类别和data目录下三个txt文件路径

代码如下

# COCO 2017 dataset http://cocodataset.org
 
# download command/URL (optional)
# download: bash ./scripts/get_coco.sh
 
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: /home/sxj/yolov7-4/data/train.txt  # 118287 images
val: /home/sxj/yolov7-4/data/val.txt  # 5000 images
test: /home/sxj/yolov7-4/data/test.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
 
# number of classes
nc: 3
 
# class names
names: ['bike','carsgraz','person']

4.修改cfg目录下/home/sxj/yolov7-4/cfg/deploy/yolov7.yaml，yolov7.yaml文件里面改成自己类别数

四、返回yolov7主目录修改train.py文件

其中 --weights', type=str, default='yolov7.pt', help='initial weights path'改成yolov7.pt文件路径

'--cfg', type=str, default='/home/sxj/yolov7-4/cfg/deploy/yolov7.yaml', help='model.yaml path')改成yolov7.yaml路径

'--data', type=str, default='data/car.yaml', help='data.yaml path'把data目录下的coco.yaml文件改成自己的路径

里面'--epochs', type=int, default=50

'--batch-size', type=int, default=1, help='total batch size for all GPUs'

'--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu or mps'

参数根据需要调整

五、完成之后运行python train.py

出现如下报错及解决方法：YOLO7报错：indices should be either on cpu or on the same device as the indexed tensor (cpu)

YOLO7报错：indices should be either on cpu or on the same device as the indexed tensor (cpu)

运行之后在runs里面找到best.pt权重文件

拷贝一份放在主目录下，打开detect.py改成自己best.pt权重文件和测试图片路径

在运行

python detect.py

在 runs/detect/exp下可查看自己模型文件测试效果即可

到此全部完成