【目标检测】2024最新-用Yolov8训练自己的数据集（保姆级教学）_新版yolov8怎么

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

pip config set install.trusted-host mirrors.aliyun.com

​

pip install -r requirements.txt

pip install ultralytics

yolo predict model=yolov8n.pt source='ultralytics/assets/bus.jpg'

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 = ['填写自己的类别']
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:/ultralytics-main/data/train.txt
val: D:/ultralytics-main/data/val.txt
test: D:/ultralytics-main/data/test.txt
 
nc: 5 # 填写类别数
 
names: ["填写自己的类别"] 
 
# train,val,test的路径根据自己情况而定

yolo train data=data/data.yaml model=yolov8s.pt epochs=300 imgsz=640 batch=8 workers=0 device=cpu