1.Yolov8介绍

         Ultralytics YOLOv8是Ultralytics公司开发的YOLO目标检测和图像分割模型的最新版本。YOLOv8是一种尖端的、最先进的（SOTA）模型，它建立在先前YOLO成功基础上，并引入了新功能和改进，以进一步提升性能和灵活性。它可以在大型数据集上进行训练，并且能够在各种硬件平台上运行，从CPU到GPU。

具体改进如下：

1. Backbone：使用的依旧是CSP的思想，不过YOLOv5中的C3模块被替换成了C2f模块，实现了进一步的轻量化，同时YOLOv8依旧使用了YOLOv5等架构中使用的SPPF模块；

2. PAN-FPN：毫无疑问YOLOv8依旧使用了PAN的思想，不过通过对比YOLOv5与YOLOv8的结构图可以看到，YOLOv8将YOLOv5中PAN-FPN上采样阶段中的卷积结构删除了，同时也将C3模块替换为了C2f模块；

3. Decoupled-Head：是不是嗅到了不一样的味道？是的，YOLOv8走向了Decoupled-Head；

4. Anchor-Free：YOLOv8抛弃了以往的Anchor-Base，使用了Anchor-Free的思想；

5. 损失函数：YOLOv8使用VFL Loss作为分类损失，使用DFL Loss+CIOU Loss作为分类损失；

6. 样本匹配：YOLOv8抛弃了以往的IOU匹配或者单边比例的分配方式，而是使用了Task-Aligned Assigner匹配方式

框架图提供见链接：Brief summary of YOLOv8 model structure · Issue #189 · ultralytics/ultralytics · GitHub

2.安全帽数据集介绍

数据集大小3241张，train:val:test 随机分配为7:2:1，类别：hat

 

2.1数据集划分

通过split_train_val.py得到trainval.txt、val.txt、test.txt  

# coding:utf-8import os
import random
import argparseparser = argparse.ArgumentParser()
#xml文件的地址，根据自己的数据进行修改 xml一般存放在Annotations下
parser.add_argument('--xml_path', default='Annotations', type=str, help='input xml label path')
#数据集的划分，地址选择自己数据下的ImageSets/Main
parser.add_argument('--txt_path', default='ImageSets/Main', type=str, help='output txt label path')
opt = parser.parse_args()trainval_percent = 0.9
train_percent = 0.7
xmlfilepath = opt.xml_path
txtsavepath = opt.txt_path
total_xml = os.listdir(xmlfilepath)
if not os.path.exists(txtsavepath):os.makedirs(txtsavepath)num = len(total_xml)
list_index = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list_index, tv)
train = random.sample(trainval, tr)file_trainval = open(txtsavepath + '/trainval.txt', 'w')
file_test = open(txtsavepath + '/test.txt', 'w')
file_train = open(txtsavepath + '/train.txt', 'w')
file_val = open(txtsavepath + '/val.txt', 'w')for i in list_index:name = total_xml[i][:-4] + '\n'if i in trainval:file_trainval.write(name)if i in train:file_train.write(name)else:file_val.write(name)else:file_test.write(name)file_trainval.close()
file_train.close()
file_val.close()
file_test.close()

1.2 通过voc_label.py得到适合yolov8需要的txt

# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets = ['train','val']
classes = ['hat']def convert(size, box):dw = 1. / size[0]dh = 1. / size[1]x = (box[0] + box[1]) / 2.0y = (box[2] + box[3]) / 2.0w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn (x, y, w, h)
def convert_annotation(image_id):in_file = open('Annotations/%s.xml' % (image_id))out_file = open('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').textcls = obj.find('name').textif cls not in classes or int(difficult) == 1:continuecls_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))bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
print(wd)
for image_set in sets:if not os.path.exists('labels/'):os.makedirs('labels/')image_ids = open('ImageSets/Main/%s.txt' % (image_set)).read().strip().split()list_file = open('%s.txt' % (image_set), 'w')for image_id in image_ids:list_file.write('images/%s.jpg\n' % (image_id))convert_annotation(image_id)list_file.close()

2.3生成内容如下

3.训练结果分析

训练结果如下：

mAP@0.5 0.897

YOLOv8n summary (fused): 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPsClass     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 55/55 [00:09<00:00,  6.04it/s]all        875       2172      0.925      0.822      0.897      0.706