YOLOv8训练自己的数据集+常用传参说明

-- 创建环境
conda create -n yolov8 python=3.8
-- 激活环境
conda activate yolov8

pip install ultralytics
--验证环境是否安装成功
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

import os
class BatchRename():
    '''
    批量重命名文件夹中的图片文件
    '''
    def __init__(self):
        self.path = 'D:\git\ultralytics\data\images'   #表示需要命名处理的文件夹
        self.new_path='D:\git\ultralytics\data\images\new'
    def rename(self):
        filelist = os.listdir(self.path)    #获取文件夹中文件的所有的文件
        total_num = len(filelist)           #获取文件长度（个数）
        i = 1  #表示文件的命名是从1开始的
        for item in filelist:
            if 1:  #初始的图片的格式为jpg格式的（或者源文件是png格式及其他格式，后面的转换格式就可以调整为自己需要的格式即可）
                src = os.path.join(os.path.abspath(self.path), item)     #连接两个或更多的路径名组件
                # dst = os.path.join(os.path.abspath(self.new_path), ''+str(i) + '.jpg')#处理后的格式也为jpg格式的，当然这里可以改成png格式
                dst = os.path.join(os.path.abspath(self.path), 'wheat' + format(str(i), '0>3s') + '.jpg')    #这种情况下的命名格式为0000000.jpg形式，可以自主定义想要的格式
                try:
                    os.rename(src, dst)    #src – 要修改的目录名      dst – 修改后的目录名
                    print('converting %s to %s ...' % (src, dst))
                    i = i + 1
                except:
                    continue
        print ('total %d to rename & converted %d jpgs' % (total_num, i))
if __name__ == '__main__':
    demo = BatchRename()
    demo.rename()

安装labelimg
pip install labelimg
启动
labelimg

import os
import random
trainval_percent = 0.9
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:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

import xml.etree.ElementTree as ET
import os
from os import getcwd
sets = ['train', 'val', 'test']
classes = ['High Ripeness','Low Ripeness','Medium Ripeness']
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('data/Annotations/%s.xml' % (image_id), encoding='UTF-8')
    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
        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('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(abs_path + 'data/images/%s.jpg\n' % (image_id))
        convert_annotation(image_id)
    list_file.close()

train: D:\git\ai\yolov8\data\train.txt
val: D:\git\ai\yolov8\data\val.txt
test: D:\git\ai\yolov8\data\test.txt
nc: 7
names:
  0: Powdery Mildew  # 白粉病
  1: Scab            # 赤霉病
  2: Leaf Rust       # 叶锈病
  3: Stripe Rust     # 条锈病
  4: Glume Blotch    # 颖枯病
  5: Wheat Ear       # 正常麦穗
  6: Wheat Leaf      # 正常麦叶

yolo train data=你的配置文件（xx.yaml）的绝对路径 model=yolov8n.pt epochs=300 imgsz=640 batch=8 workers=0 device=0

from ultralytics import YOLO
if __name__ == '__main__':
    # 加载模型
    model = YOLO("D:/git/ultralytics/ultralytics/cfg/models/v8/yolov8.yaml")
    # 训练模型
    results = model.train(data="D:/git/ultralytics/data/wheat.yaml",  
                          resume=True,
                          epochs=100,
                          project='wheat',
                          patience=30,
                          name='wheat-yolov8n-100',
                          amp=False)