做策划的网站推广,郑州app定制开发,企业网站模板html下载,国家高新技术企业名单1、环境搭建
https://github.com/ultralytics/yolov5
2、安装需要的软件 pip install -U -r requirements.txt
3、准备数据 在data文件下建立上面三个文件#xff08;Annotations、images与ImageSets#xff0c;labels后续我们脚本生成#xff09;其中Annotations存放xml…1、环境搭建
https://github.com/ultralytics/yolov5
2、安装需要的软件 pip install -U -r requirements.txt
3、准备数据 在data文件下建立上面三个文件Annotations、images与ImageSetslabels后续我们脚本生成其中Annotations存放xml文件images图像ImageSets新建Main文件存放train与test文件脚本生成labels是标签文件
划分训练集与测试集为了充分利用数据集我们只划分这两个生成的在ImageSets / Main文件下 import os
import randomtrainval_percent 0.2 #可自行进行调节
train_percent 1
xmlfilepath Annotations
txtsavepath ImageSets\Main
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(ImageSets/Main/trainval.txt, w)
ftest open(ImageSets/Main/test.txt, w)
ftrain open(ImageSets/Main/train.txt, w)
#fval open(ImageSets/Main/val.txt, w)for i in list:name total_xml[i][:-4] \nif 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()
建立voc_labels文件生成labels标签文件
import xml.etree.ElementTree as ETimport pickleimport osfrom os import listdir, getcwdfrom os.path import joinsets [train, test]classes [apple,orange] #自己训练的类别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(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).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()for image_set in sets:if not os.path.exists(data/labels/):os.makedirs(data/labels/)image_ids open(data/ImageSets/Main/%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()
4、配置训练文件
在data目录下新建fruit.yaml配置训练的数据
# COCO 2017 dataset http://cocodataset.org# Download command: bash yolov5/data/get_coco2017.sh# Train command: python train.py --data ./data/coco.yaml# Dataset should be placed next to yolov5 folder:# /parent_folder# /coco# /yolov5# train and val datasets (image directory or *.txt file with image paths)train: xx/xx/train2017.txt # 上面我们生成的train根据自己的路径进行更改val: xx/xx/val2017.txt # 上面我们生成的test#test: ../coco/test-dev2017.txt # 20k images for submission to https://competitions.codalab.org/competitions/20794# number of classesnc: 2 #训练的类别# class namesnames: [apple,orange]# Print classes# with open(data/coco.yaml) as f:# d yaml.load(f, Loaderyaml.FullLoader) # dict# for i, x in enumerate(d[names]):# print(i, x)
models文件采用那个yaml我们更改那个例如采用yolov5s.yaml # parametersnc: 2 # number of classes 训练的类别数depth_multiple: 0.33 # model depth multiplewidth_multiple: 0.50 # layer channel multiple# anchorsanchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# yolov5 backbonebackbone:# [from, number, module, args][[-1, 1, Focus, [64, 3]], # 1-P1/2[-1, 1, Conv, [128, 3, 2]], # 2-P2/4[-1, 3, Bottleneck, [128]],[-1, 1, Conv, [256, 3, 2]], # 4-P3/8[-1, 9, Bottleneck, [256]],[-1, 1, Conv, [512, 3, 2]], # 6-P4/16[-1, 9, Bottleneck, [512]],[-1, 1, Conv, [1024, 3, 2]], # 8-P5/32[-1, 1, SPP, [1024, [5, 9, 13]]],[-1, 3, Bottleneck, [1024]], # 10]# yolov5 headhead:[[-1, 3, Bottleneck, [1024, False]], # 11[-1, 1, nn.Conv2d, [na * (nc 5), 1, 1, 0]], # 12 (P5/32-large)[-2, 1, nn.Upsample, [None, 2, nearest]],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 1, Conv, [512, 1, 1]],[-1, 3, Bottleneck, [512, False]],[-1, 1, nn.Conv2d, [na * (nc 5), 1, 1, 0]], # 17 (P4/16-medium)[-2, 1, nn.Upsample, [None, 2, nearest]],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 1, Conv, [256, 1, 1]],[-1, 3, Bottleneck, [256, False]],[-1, 1, nn.Conv2d, [na * (nc 5), 1, 1, 0]], # 22 (P3/8-small)[[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
5、训练
python train.py --data data/fruit.yaml --cfg models/yolov5s.yaml --weights --batch-size 16 --epochs 100
这里想加载预训练权重要更改下代码不然会报错
train.py中115行日期2020.6.9
try:#ckpt[model] \#{k: v for k, v in ckpt[model].state_dict().items() if model.state_dict()[k].numel() v.numel()}ckpt[model] \{k: v for k, v in ckpt[model].state_dict().items() if k in model.state_dict().keys()\and model.state_dict()[k].numel() v.numel()
训练命令
python train.py --data data/fruit.yaml --cfg models/yolov5s.yaml --weights weights/yolov5s.pt --batch-size 16 --epochs 100
6、测试
python detect.py --source file.jpg # imagefile.mp4 # video./dir # directory0 # webcamrtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp streamhttp://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
跟大家点小建议自己去尝试。
YOLO V5建议如果需要训练较小的自定义数据集Adam是更合适的选择尽管Adam的学习率通常比SGD低。但是如果训练大型数据集对于YOLOV5来说SGD效果比Adam好
官方指南你值得拥有https://github.com/ultralytics/yolov5
