yolov5訓練自己的資料集

下載下傳yolov5

https://github.com/ultralytics/yolov5

配置環境

建立一個pytoych的虛拟環境

conda create -n torchv5 python == 3.7

           

激活虛拟環境

conda activate torchv5

           

requirements.txt 包含yolov5 所需的全部包，讀取該檔案安裝即可

采用清華源安裝，非常快！

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r E:\Project\yolov5-master\requirements.txt
           

資料集

在data下建立建立幾個檔案夾如圖所示，把自己的資料集中圖檔放到JPEGImages,标簽放到Annotations,再把JPEGImages中的圖檔複制到images中。

根目錄建立make_txt.py

import os
import random
trainval_percent = 0.1
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:
            ftest.write(name)
        else:
            fval.write(name)
    else:
        ftrain.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

           

根目錄建立2voc_label.py

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets = ['train', 'test','val']
classes = ['person']
def convert(size, box):
    dw = 1. / size[0]
    dh = 1. / size[1]
    x = (box[0] + box[1]) / 2.0
    y = (box[2] + box[3]) / 2.0
    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))
    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
        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))
        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('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('data/images/%s.jpg\n' % (image_id))
        convert_annotation(image_id)
    list_file.close()

           

依次運作，即可生成所需訓練檔案

修改模型參數

修改coco.yaml為自己資料集需要的形式，比如person.yaml

修改models 下的yaml 檔案

yolov5總共有四個模型，用哪個就修改那個，以yolov5s.yaml為例

，隻需修改 nc 為自己資料集的類别數即可

接下來修改train.py 的參數

cfg , data ,epochs ,img_size ,batch_size , weigths(初始權重)

連結：https://pan.baidu.com/s/13WZJ8gdB_QrkF9o9JTYnDA

提取碼：g4ei

訓練

運作train.py