一、準備深度學習環境

下載yolov7代碼

下載完成解壓放在自己的主目錄

命名yolov7-4

二、?準備自己的數據集

1.進入主目錄

2.進入data目錄下把你的xml文件夾命名為Annotations，把你的存放圖片文件夾命名為images

3.分別新建ImageSets、imagtest（里面存放測試圖片）、labels（里面存放轉換之后的yolo格式文件）

三、 1.2.在data目錄下新建split_train_val.py文件

里面內容如下

# coding:utf-8import os
import random
import argparseparser = argparse.ArgumentParser()
#xml文件的地址，根據自己的數據進行修改 xml一般存放在Annotations下
parser.add_argument('--xml_path', default='Annotations', type=str, help='input xml label path')
#數據集的劃分，地址選擇自己數據下的ImageSets/Main
parser.add_argument('--txt_path', default='ImageSets/Main', type=str, help='output txt label path')
opt = parser.parse_args()trainval_percent = 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()

運行之后會在ImageSets/Main下生成四個.txt文件

2.在data目錄下新建voc_label.py文件，里面存放代碼，里面classes需要改成自己的類別

# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import os
from os import getcwdsets = ['train', 'val', 'test']
classes = ['bike','carsgraz','person']   # 改成自己的類別
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 - 1y = (box[2] + box[3]) / 2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn x, y, w, hdef 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()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))b1, b2, b3, b4 = b# 標注越界修正if b2 > w:b2 = wif b4 > h:b4 = hb = (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('./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(abs_path + '/images/%s.png\n' % (image_id)) # 注意你的圖片格式，如果是.jpg記得修改convert_annotation(image_id)list_file.close()

3.拷貝一份coco.yaml文件里面改成自己的類別和data目錄下三個txt文件路徑

代碼如下

# COCO 2017 dataset http://cocodataset.org# download command/URL (optional)
# download: bash ./scripts/get_coco.sh# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: /home/sxj/yolov7-4/data/train.txt  # 118287 images
val: /home/sxj/yolov7-4/data/val.txt  # 5000 images
test: /home/sxj/yolov7-4/data/test.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794# number of classes
nc: 3# class names
names: ['bike','carsgraz','person']

4.修改cfg目錄下/home/sxj/yolov7-4/cfg/deploy/yolov7.yaml，yolov7.yaml文件里面改成自己類別數

四、返回yolov7主目錄修改train.py文件

其中 --weights', type=str, default='yolov7.pt', help='initial weights path'改成yolov7.pt文件路徑

'--cfg', type=str, default='/home/sxj/yolov7-4/cfg/deploy/yolov7.yaml', help='model.yaml path')改成yolov7.yaml路徑

'--data', type=str, default='data/car.yaml', help='data.yaml path'把data目錄下的coco.yaml文件改成自己的路徑

里面'--epochs', type=int, default=50

'--batch-size', type=int, default=1, help='total batch size for all GPUs'

'--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu or mps'

參數根據需要調整

五、完成之后運行python train.py

出現如下報錯及解決方法：YOLO7報錯：indices should be either on cpu or on the same device as the indexed tensor (cpu)

YOLO7報錯：indices should be either on cpu or on the same device as the indexed tensor (cpu)

運行之后在runs里面找到best.pt權重文件

拷貝一份放在主目錄下，打開detect.py改成自己best.pt權重文件和測試圖片路徑

在運行

python?detect.py

在?runs/detect/exp下可查看自己模型文件測試效果即可

到此全部完成