yolov5参数修改参数使用gpu

一、环境配置

1. 下载Anaconda，进入conda ，利用conda create -n  xxx创建自己的环

conda create -n yolov8 python=3.9
conda activate yolov8

1. 装pytorch，进入pytorch官网，根据需要进行选择，后复制命令至conda
2. 进入官网下载yolov8至指定文件夹
3. 在conda中进入该文件夹
4. 执行以下代码配置yolov8环境

pip install ultralytics
pip install -r requirements.txt

1.   至此环境搭建完毕，在yolov8所在环境中执行以下conda命令，进行验证

二、数据集标注

参考以下内容

labelme的安装及使用

三、训练 

分配数据集

1. 在yolov8中创建存放数据集文件夹  dataset
2. 将labelme标注的json文件格式转为txt格式

# -*- coding: utf-8 -*-
import json
import os
import argparse
from tqdm import tqdm
import glob
import cv2
import numpy as np


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)
        # print(path)
        with open(path, 'r') as load_f:
            print(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)

# 填写json文件地址
# 填写txt文件格式
# 填写类别名称

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='json convert to txt params')
    parser.add_argument('--json-dir', type=str, default='dataset/labelJson', help='json path dir')
    parser.add_argument('--save-dir', type=str, default='dataset/labelTxt', help='txt save dir')
    parser.add_argument('--classes', type=str, default='YL,DEFECT,TongXinYuan', 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)

1. 在ultralytics下创建mydata文件夹存放训练和验证图像
2. 进行数据集的划分，splitData.py

# -*- coding:utf-8 -*
import os
import random
import os
import shutil
def data_split(full_list, ratio):

    n_total = len(full_list)
    offset = int(n_total * ratio)
    if n_total == 0 or offset < 1:
        return [], full_list
    random.shuffle(full_list)
    sublist_1 = full_list[:offset]
    sublist_2 = full_list[offset:]
    return sublist_1, sublist_2


train_p="ultralytics/mydata/train"      #训练集路径
val_p="ultralytics/mydata/val"          #验证集路径
imgs_p="images"                         #存放图像的文件夹名称
labels_p="labels"                       #存放标签的文件夹名称

#创建训练集
if not os.path.exists(train_p):#指定要创建的目录
    os.mkdir(train_p)
tp1=os.path.join(train_p,imgs_p)
tp2=os.path.join(train_p,labels_p)
print(tp1,tp2)
if not os.path.exists(tp1):#指定要创建的目录
    os.mkdir(tp1)
if not os.path.exists(tp2):  # 指定要创建的目录
    os.mkdir(tp2)

#创建测试集文件夹
if not os.path.exists(val_p):#指定要创建的目录
    os.mkdir(val_p)
vp1=os.path.join(val_p,imgs_p)
vp2=os.path.join(val_p,labels_p)
print(vp1,vp2)
if not os.path.exists(vp1):#指定要创建的目录
    os.mkdir(vp1)
if not os.path.exists(vp2):  # 指定要创建的目录
    os.mkdir(vp2)

#数据集路径
images_dir="dataset/images"
labels_dir="dataset/labelTxt"
#划分数据集，设置数据集数量占比
proportion_ = 0.9 #训练集占比

total_file = os.listdir(images_dir)

num = len(total_file)  # 统计所有的标注文件
list_=[]
for i in range(0,num):
    list_.append(i)

list1,list2=data_split(list_,proportion_)

for i in range(0,num):
    file=total_file[i]
    print(i,' - ',total_file[i])
    name=file.split('.')[0]
    if i in list1:
        jpg_1 = os.path.join(images_dir, file)
        jpg_2 = os.path.join(train_p, imgs_p, file)
        txt_1 = os.path.join(labels_dir, name + '.txt')
        txt_2 = os.path.join(train_p, labels_p, name + '.txt')
        if os.path.exists(txt_1) and os.path.exists(jpg_1):
            shutil.copyfile(jpg_1, jpg_2)
            shutil.copyfile(txt_1, txt_2)
        elif os.path.exists(txt_1):
            print(txt_1)
        else:
            print(jpg_1)

    elif i in list2:
        jpg_1 = os.path.join(images_dir, file)
        jpg_2 = os.path.join(val_p, imgs_p, file)
        txt_1 = os.path.join(labels_dir, name + '.txt')
        txt_2 = os.path.join(val_p, labels_p, name + '.txt')
        shutil.copyfile(jpg_1, jpg_2)
        shutil.copyfile(txt_1, txt_2)

print("数据集划分完成： 总数量：",num," 训练集数量：",len(list1)," 验证集数量：",len(list2))

 参数配置修改

1. deault.yaml中有各种训练、验证、测试的默认配置参数，所在位置如图所示。使用windows进行训练的将文件中的workers改为0，否则报错
2. 
   
3. 创建数据配置文件mycoco128-seg.yaml，该文件由coco128-seg.yaml修改得来
4. 
   
5. 修改yolov-seg.yaml中的nc
6. 
   

训练

1. 创建一个python文件myTrain，并对参数进行设置否则采用默认，运行该文件即可开始训练，其参数如下表

from ultralytics import YOLO

# Load a model
model = YOLO('ultralytics/cfg/models/v8/yolov8-seg.yaml')  # build a new model from YAML
model = YOLO('yolov8s-seg.pt')  # load a pretrained model (recommended for training)
model = YOLO('ultralytics/cfg/models/v8/yolov8-seg.yaml').load('yolov8s-seg.pt')  # build from YAML and transfer weights

# Train the model
results = model.train(data='ultralytics/cfg/datasets/mycoco128-seg.yaml', epochs=100, batch=4, imgsz=640, workers=0)

验证

创建一个python文件myVal，对训练模型进行验证，参数如下表，不进行设置则采用默认

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8s-seg.pt')  # load an official model
model = YOLO('runs/segment/train8/weights/best.pt')  # load a custom model

# Validate the model
metrics = model.val(batch=16, conf=0.01, iou=0.5)  # no arguments needed, dataset and settings remembered
metrics.box.map    # map50-95
metrics.box.map50  # map50
metrics.box.map75  # map75
metrics.box.maps   # a list contains map50-95 of each category

预测 

创建一个python文件myPredict，对参数进行设置后运行该文件即可

from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('runs/segment/train8/weights/best.pt')

# Run inference on 'bus.jpg' with arguments
model.predict('ultralytics/mydata/val/images', save=True,conf=0.25, iou=0.7)

其他具体操作可看官方文档Home - Ultralytics YOLOv8 Docs