- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊 | 接辅导、项目定制
拉取项目
git clone https://github.com/ultralytics/ultralytics
安装依赖
cd ultralytics
pip install -r requirement.txt
pip install -e .
准备数据集
下载数据集zip包，并解压，数据集的地址在原作者博客中有。
unzip archive (3).zip
mv archive (3) fruit_data
制作数据集
以下操作全部在fruit_data目录下
cd fruit_data
生成图片列表，划分数据集
使用脚本split_train_val.py，从标注xml文件中抽取出图像的列表和标签信息，并保存到相应的文件中。
#!/usr/bin/env python
# coding: utf-8
import os
import random
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--xml_path', default='annotations', type=str, help='input xml label path')
parser.add_argument('--txt_path', default='imageSets/Main', type=str, help='output txt label path')
opt = parser.parse_args()
trainval_percent = 1.0
train_percent = 0.9
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()
python split_train_val.py
生成VOC格式的数据文件
因为YOLO框架使用的是VOC格式的数据集，因此需要生成一个VOC格式的数据文件
使用脚本voc_label.py
#!/usr/bin/env python
# coding: utf-8
import xml.etree.ElementTree as ET
import os
from os import getcwd
sets = ['train', 'val', 'test']
classes = ['banana', 'snake fruit', 'pineapple', 'dragon fruit']
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('./annotations/%s.xml' % (image_id), encoding='UTF-8')
out_file = open('./labels/%s.txt' % (image_id), 'w')
tree = ET.parse(in_file)
root = tree.getroot()
filename = root.find('filename').text
filenameFormat = filename.split('.')[1]
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
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
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')
return filenameFormat
wd = getcwd()
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:
filenameFormat = convert_annotation(image_id)
list_file.write(abs_path + '/images/%s.%s\n' % (image_id, filenameFormat))
list_file.close()
python voc_label.py
编写数据集配置文件
在项目根目录下创建一个文件data.yaml
cd ..
vim data.yaml
配置文件内容如下
train: ./data/train.txt
val: ./data/val.txt
# number of classes
nc: 4
# 类别名
names: ['banana', 'snake fruit', 'pineapple', 'dragon fruit']
开始训练
yolo task=detect mode=train model=yolov8s.yaml data=/root/autodl_tmp/ultralytics/data.yaml epochs=100 batch=4
训练过程如下：
训练结果
通过上面训练结束可以看出，总体上达到了98.7%的准确率，99.7%的召回率，效果还是非常不错的。
训练过程如图
训练结果如图
