【保姆级教程】YOLOv8目标检测：训练自己的数据集

一、YOLOV8环境准备

1.1 下载安装最新的YOLOv8代码

 仓库地址： https://github.com/ultralytics/ultralytics

1.2 配置环境

  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

二、数据准备

2.1 安装labelme标注软件

pip install labelme

2.1.2 打开LabelImg软件

使用Anaconda Prompt启动labeme标注工具

在这里插入图片描述

2.2 标注自己的数据

在这里插入图片描述

2.3 数据处理

2.3.1 运行下面代码，将json格式标签转为txt格式标签

先创建txt文件夹，整体目录如下
在这里插入图片描述

import json
import osname2id = {
"-":0,
"0":1,
"1":2,
"2":3,
"3":4,
"4":5,
"5":6,
"6":7,
"7":8,
"8":9,
"9":10,
"A":11,
"B":12,
"C":13,
"D":14,
"E":15,
"F":16,
"G":17,
"H":18,
"I":19,
"J":20,
"K":21,
"L":22,
"M":23,
"N":24,
"O":25,
"P":26,
"Q":27,
"R":28,
"S":29,
"T":30,
"U":31,
"V":32,
"W":33,
"X":34,
"Y":35,
"Z":36}def convert(img_size, box):dw = 1./(img_size[0])dh = 1./(img_size[1])x = (box[0] + box[2])/2.0 - 1y = (box[1] + box[3])/2.0 - 1w = box[2] - box[0]h = box[3] - box[1]x = x*dww = w*dwy = y*dhh = h*dhx = round(x, 3)w = round(w, 3)y = round(y, 3)h = round(h, 3)return (x,y,w,h)def decode_json(json_floder_path,json_name):# 需要手动新建下面目录的文件夹，根据自己实际情况而定txt_name = 'D:\\data\\slot-number\\wanda\\txt\\' + json_name[0:-5] + '.txt'txt_file = open(txt_name, 'w')json_path = os.path.join(json_floder_path, json_name)data = json.load(open(json_path, 'r', encoding='gb2312'))img_w = data['imageWidth']img_h = data['imageHeight']for i in data['shapes']:label_name = i['label']if (i['shape_type'] == 'rectangle'):x1 = int(i['points'][0][0])y1 = int(i['points'][0][1])x2 = int(i['points'][1][0])y2 = int(i['points'][1][1])bb = (x1,y1,x2,y2)bbox = convert((img_w,img_h),bb)txt_file.write(str(name2id[label_name]) + " " + " ".join([str(a) for a in bbox]) + '\n')if __name__ == "__main__":# D:\\data\\slot-number\\wanda\\json存放了labelme标注生成的json文件的文件夹json_floder_path = 'D:\\data\\slot-number\\wanda\\json'json_names = os.listdir(json_floder_path)for json_name in json_names:decode_json(json_floder_path,json_name)

查看转换后生成的yolo格式的txt标签文件
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2.3.2 运行下面代码，划分数据集

在这里插入图片描述

# 将图片和标注数据按比例切分为 训练集和测试集
import shutil
import random
import os
import argparse# 检查文件夹是否存在
def mkdir(path):if not os.path.exists(path):os.makedirs(path)def main(image_dir, txt_dir, save_dir):# 创建文件夹mkdir(save_dir)images_dir = os.path.join(save_dir, 'images')labels_dir = os.path.join(save_dir, 'labels')img_train_path = os.path.join(images_dir, 'train')img_test_path = os.path.join(images_dir, 'test')img_val_path = os.path.join(images_dir, 'val')label_train_path = os.path.join(labels_dir, 'train')label_test_path = os.path.join(labels_dir, 'test')label_val_path = os.path.join(labels_dir, 'val')mkdir(images_dir);mkdir(labels_dir);mkdir(img_train_path);mkdir(img_test_path);mkdir(img_val_path);mkdir(label_train_path);mkdir(label_test_path);mkdir(label_val_path);# 数据集划分比例，训练集75%，验证集15%，测试集15%，按需修改train_percent = 0.8val_percent = 0.1test_percent = 0.1total_txt = os.listdir(txt_dir)num_txt = len(total_txt)list_all_txt = range(num_txt)  # 范围 range(0, num)num_train = int(num_txt * train_percent)num_val = int(num_txt * val_percent)num_test = num_txt - num_train - num_valtrain = random.sample(list_all_txt, num_train)# 在全部数据集中取出trainval_test = [i for i in list_all_txt if not i in train]# 再从val_test取出num_val个元素，val_test剩下的元素就是testval = random.sample(val_test, num_val)print("训练集数目：{}, 验证集数目：{},测试集数目：{}".format(len(train), len(val), len(val_test) - len(val)))for i in list_all_txt:name = total_txt[i][:-4]srcImage = os.path.join(image_dir, name + '.png')srcLabel = os.path.join(txt_dir, name + '.txt')if i in train:dst_train_Image = os.path.join(img_train_path, name + '.png')dst_train_Label = os.path.join(label_train_path, name + '.txt')shutil.copyfile(srcImage, dst_train_Image)shutil.copyfile(srcLabel, dst_train_Label)elif i in val:dst_val_Image = os.path.join(img_val_path, name + '.png')dst_val_Label = os.path.join(label_val_path, name + '.txt')shutil.copyfile(srcImage, dst_val_Image)shutil.copyfile(srcLabel, dst_val_Label)else:dst_test_Image = os.path.join(img_test_path, name + '.png')dst_test_Label = os.path.join(label_test_path, name + '.txt')shutil.copyfile(srcImage, dst_test_Image)shutil.copyfile(srcLabel, dst_test_Label)if __name__ == '__main__':parser = argparse.ArgumentParser(description='split datasets to train,val,test params')parser.add_argument('--image-dir', type=str,default='D:/data/slot-number/wanda/image', help='image path dir')parser.add_argument('--txt-dir', type=str,default='D:/data/slot-number/wanda/txt' , help='txt path dir')parser.add_argument('--save-dir', default='D:/study/cnn/yolo/ultralytics/dataset',type=str, help='save dir')args = parser.parse_args()image_dir = args.image_dirtxt_dir = args.txt_dirsave_dir = args.save_dirmain(image_dir, txt_dir, save_dir)

2.3.3 使用脚本，获取训练集，验证集的txt标签的路径

三、配置文件设置

3.1 修改coco8.yaml

修改ultralytics\ultralytics\cfg\datasets\coco8.yaml配置文件内容：（建议使用绝对路径）

path: D:/data/slot-number/
train: images/train
val: images/val
test:names:0: sign -1: nubmer 02: nubmer 13: nubmer 24: nubmer 35: nubmer 46: nubmer 57: nubmer 68: nubmer 79: nubmer 810: nubmer 911: A12: B13: C14: D15: E16: F17: G18: H19: I20: J21: K22: L23: M24: NN25: O26: P27: Q28: R29: S30: T31: U32: V33: W34: X35: YY36: Z