yolov8_seg分割模型训练自己数据集

MFT小白

已于 2023-06-06 15:53:02 修改

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分类专栏：机器视觉文章标签： YOLO 深度学习机器学习

于 2023-04-27 14:30:00 首次发布

本文链接：https://blog.youkuaiyun.com/zhangbing0116/article/details/130404975

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该文介绍了如何使用Yolov8模型进行实例分割检测。首先，通过git克隆官方源码，然后进行数据预处理，包括将json标签转换为txt格式并按比例划分训练、验证和测试数据集。接着，配置my_seg.yaml文件以设定训练参数，并对yolov8-seg.yaml进行修改以适应自定义类别。最后，启动训练过程。

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基本思想：业务需求，需要实例分割检测案例，选定yolov8模型训练自己业务场景数据集。

一、拉取官方源码

git clone https://github.com/ultralytics/ultralytics

二、数据准备

数据及结构

对数据集进行转换和划分。注意：将图片和json文件放在不同的文件夹里。如下图所示，另外新建两个文件夹txt和split文件夹。

注意标签是：类别 x坐标 y坐标 …（中间有空格，并且坐标除以了原图图片大小，归一化后的）这是实例分割的数据要求。

my_datasets
├── images    # 原始图片
├── json      # 对应json文件
├── split     # 划分后的数据集
│   ├── images
│   │   ├── test
│   │   ├── train
│   │   └── val
│   └── labels
│       ├── test
│       ├── train
│       └── val
└── txt    # 存放标签 （归一化后的值） 形式：ids x y x y ...

2.1 将json文件转换为txt格式

json2txt.py

# -*- coding: utf-8 -*-
import json
import os
import argparse
from tqdm import tqdm
 
 
def convert_label_json(json_dir, save_dir, classes):
    json_paths = os.listdir(json_dir)
    classes = classes.split(',')
 
    for json_path in tqdm(json_paths):
        # for json_path in json_paths:
        path = os.path.join(json_dir, json_path)
        with open(path, 'r') as load_f:
            json_dict = json.load(load_f)
        h, w = json_dict['imageHeight'], json_dict['imageWidth']
 
        # save txt path
        txt_path = os.path.join(save_dir, json_path.replace('json', 'txt'))
        txt_file = open(txt_path, 'w')
 
        for shape_dict in json_dict['shapes']:
            label = shape_dict['label']
            label_index = classes.index(label)
            points = shape_dict['points']
 
            points_nor_list = []
 
            for point in points:
                points_nor_list.append(point[0] / w)
                points_nor_list.append(point[1] / h)
 
            points_nor_list = list(map(lambda x: str(x), points_nor_list))
            points_nor_str = ' '.join(points_nor_list)
 
            label_str = str(label_index) + ' ' + points_nor_str + '\n'
            txt_file.writelines(label_str)
 
 
if __name__ == "__main__":
    """
    python json2txt_nomalize.py --json-dir my_datasets/color_rings/jsons --save-dir my_datasets/color_rings/txts --classes "cat,dogs"
    """
    parser = argparse.ArgumentParser(description='json convert to txt params')
    parser.add_argument('--json-dir', type=str,default='/home/ubuntu/ultralytics/data/json', help='json path dir')
    parser.add_argument('--save-dir', type=str,default='/home/ubuntu/ultralytics/data/txt' ,help='txt save dir')
    parser.add_argument('--classes', type=str, default='person,road,pallet',help='classes')
    args = parser.parse_args()
    json_dir = args.json_dir
    save_dir = args.save_dir
    classes = args.classes
    convert_label_json(json_dir, save_dir, classes)

2.2 划分数据集（train，test，val）

# 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 = 0.9
    val_percent = 0.1
    test_percent = 0
 
    total_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_val
 
    train = random.sample(list_all_txt, num_train)
    # 在全部数据集中取出train
    val_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test
    val = 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 + '.jpg')
        srcLabel = os.path.join(txt_dir, name + '.txt')
 
        if i in train:
            dst_train_Image = os.path.join(img_train_path, name + '.jpg')
            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 + '.jpg')
            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 + '.jpg')
            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__':
    """
    python split_datasets.py --image-dir my_datasets/color_rings/imgs --txt-dir my_datasets/color_rings/txts --save-dir my_datasets/color_rings/train_data
    """
    parser = argparse.ArgumentParser(description='split datasets to train,val,test params')
    parser.add_argument('--image-dir', type=str,default='/home/ubuntu/ultralytics/data/images', help='image path dir')
    parser.add_argument('--txt-dir', type=str,default='/home/ubuntu/ultralytics/data/txt' , help='txt path dir')
    parser.add_argument('--save-dir', default='/home/ubuntu/ultralytics/data/split', type=str, help='save dir')
    args = parser.parse_args()
    image_dir = args.image_dir
    txt_dir = args.txt_dir
    save_dir = args.save_dir
 
    main(image_dir, txt_dir, save_dir)

三、训练模型

3.1 需要新建一个my_seg.yaml文件

ubuntu@ubuntu:~$ cd ultralytics/ultralytics/datasets/

path: /home/ubuntu/ultralytics/data/split
train: /home/ubuntu/ultralytics/data/split/images/train  # train images (relative to 'path') 128 images
val: /home/ubuntu/ultralytics/data/split/images/val  # val images (relative to 'path') 128 images
test: # test images (optional)
 
# Classes
names:
  0: person
  1: road
  2: pallet

3.2 训练参数设置

3.2.1 修改yolov8-seg.yaml文件

vim ./ultralytics/models/v8/seg/yolov8-seg.yaml

nc: 3 #自己训练的内别总个数

3.2.2 修改defaul.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training

task: segment  # YOLO task, i.e. detect, segment, classify, pose
mode: train  # YOLO mode, i.e. train, val, predict, export, track, benchmark

# Train settings -------------------------------------------------------------------------------------------------------
model: /home/ubuntu/ultralytics/ultralytics/models/v8/yolov8-seg.yaml # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: /home/ubuntu/ultralytics/ultralytics/datasets/my_seg.yaml # path to data file, i.e. coco128.yaml
epochs: 100  # number of epochs to train for
patience: 50  # epochs to wait for no observable improvement for early stopping of training
batch: 8  # number of images per batch (-1 for AutoBatch)
imgsz: 640  # size of input images as integer or w,h

...

3.3.3 开始训练

ubuntu@ubuntu:~/ultralytics$ python ultralytics/yolo/v8/segment/train.py