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)