YOLOv3训练自定义数据集

2023-04-19 22:07| 来源: 网络整理| 查看: 265

1. 下载YOLOv3和权重文件

YOLOv3:git clone https://github.com/pjreddie/darknet yolov3.weights：wget https://pjreddie.com/media/files/yolov3.weights darknet53.conv.74：wget https://pjreddie.com/media/files/darknet53.conv.74

2. 数据集处理

（1）在scripts文件夹下按如下目录创建VOCdevkit 文件夹，放自己的训练数据。

VOCdevkit --VOC2007 ----Annotations #（XML标签文件） ----ImageSets ------Main ----JPEGImages # (原始图片)

（2）运行voc2yolo5.py 生成划分的训练集、测试集等文件

import os import random import sys root_path = './scripts/VOCdevkit/VOC2007' xmlfilepath = root_path + '/Annotations' txtsavepath = root_path + '/ImageSets/Main' if not os.path.exists(root_path): print("cannot find such directory: " + root_path) exit() if not os.path.exists(txtsavepath): os.makedirs(txtsavepath) trainval_percent = 0.8 train_percent = 0.2 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) print("train and val size:", tv) print("train size:", tr) ftrainval = open(txtsavepath + '/trainval.txt', 'w') ftest = open(txtsavepath + '/test.txt', 'w') ftrain = open(txtsavepath + '/train.txt', 'w') fval = open(txtsavepath + '/val.txt', 'w') for i in list: name = total_xml[i][:-4] + '\n' if i in trainval: ftrainval.write(name) if i in train: ftrain.write(name) else: fval.write(name) else: ftest.write(name) ftrainval.close() ftrain.close() fval.close() ftest.close()

（3）修改scripts/voc_label.py并运行,生成对应图片的labels标签文件

sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] classes = ["smoke","fire","xxx1",,"xxx2",,"xxx3",,"xxx4"] #按自己的类别修改，顺序要和data/voc.names保持一致

以上2步运行后目录如下所示：

VOCdevkit --VOC2007 ----Annotations #（XML标签文件） ----ImageSets ------Main ------------test.txt ------------train.txt ------------trainval.txt ------------val.txt ----JPEGImages # (原始图片) ----labels ------------00001.txt ------------00002.txt ------------00003.txt ------------...... ------------...... ------------xxxxx.txt 3. 修改配置文件 3.1 修改data/voc.names smoke fire xxx1 xxx2 xxx3 xxx4 3.2 修改cfg/voc.data classes=6 # 类别的数量 # 数据集处理中的voc_label.py生成 train=./scripts/2007_train.txt # 训练过程中训练数据的txt文件 valid=./scripts/2007_val.txt # 训练过程中验证数据的txt文件 names=data/voc.names # 类别标签名称 backup=backup/ # 存放权重的路径 3.3 修改cfg/yolov3.cfg [yolo] mask = 3,4,5 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 classes=6 # 修改为自己的类别

修改filters个数：（classes + 5）*3；修改类别数classes （共mask = 0,1,2 ; mask = 3,4,5 ; mask = 6,7,8 三处的class和上方的三处filters）

[convolutional] size=1 stride=1 pad=1 filters=33 # filters = (classes + 5）*3 activation=linear [yolo] mask = 0,1,2 anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 classes=6 # 自己的类别 num=9 jitter=.3 ignore_thresh = .7 4.修改Makefile文件 4.1修改 cd darknet vi Makefile GPU=1 #使用GPU设置为1，CPU设置为0 CUDNN=1 #使用CUDNN设置为1，否则为0 OPENCV=0 #调用摄像头，还需要设置OPENCV为1，否则为0 OPENMP=0 #使用OPENMP设置为1，否则为0 DEBUG=0 #使用DEBUG设置为1，否则为0 ARCH= -gencode arch=compute_80,code=sm_80 # -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated? # This is what I use, uncomment if you know your arch and want to specify # ARCH= -gencode arch=compute_52,code=compute_52 VPATH=./src/:./examples SLIB=libdarknet.so ALIB=libdarknet.a EXEC=darknet OBJDIR=./obj/ CC=gcc CPP=g++ NVCC=/usr/local/cuda-11.1/bin/nvcc #修改为自己的路径 4.2 开始编译 make

编译小结在这里插入图片描述原因：计算机显卡型号和算力需要在makefile里进行匹配解决方法：本文为RTX3080，对应computer_80，makefile里没有写，添加-gencode arch=compute_80,code=sm_80并把之前的都删去。

# Tesla A100 (GA100), DGX-A100, RTX 3080 # ARCH= -gencode arch=compute_80,code=sm_80 # Tesla V100 # ARCH= -gencode arch=compute_70,code=sm_70 # GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores # ARCH= -gencode arch=compute_75,code=sm_75 # Jetson XAVIER # ARCH= -gencode arch=compute_72,code=sm_72 # GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4 # ARCH= -gencode arch=compute_61,code=sm_61 # GP100/Tesla P100 - DGX-1 # ARCH= -gencode arch=compute_60,code=sm_60 # For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment: # ARCH= -gencode arch=compute_53,code=sm_53 # For Jetson Tx2 or Drive-PX2 uncomment: # ARCH= -gencode arch=compute_62,code=sm_62 5.训练 ./darknet detector train cfg/voc.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0

在这里插入图片描述训练小结可能是GPU被占用或者需要修改yolov3.cfg文件中的subdivision（变大）、width和height（变小）的大小

6.测试

测试

./darknet detector valid cfg/voc.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0

会在results文件夹下生成各个类别对应的.txt文件计算mAP （1）下载voc_eval.py：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py （2）新建compute_AP.py（计算单类别AP）

from voc_eval import voc_eval rec,prec,ap=voc_eval('/data/darknet/results/{}.txt', '/data/darknet/scripts/VOCdevkit/VOC2007/Annotations/{}.xml', '/data/darknet/scripts/VOCdevkit/VOC2007/ImageSets/Main/test.txt', 'smoke', '.')

注意修改voc_eval中对应的路径！应使用python2运行，若使用python3，需要把cPickle改为pickle! （3）新建compute_mAP.py（计算多类别mAP）

from voc_eval import voc_eval import os current_path = os.getcwd() results_path = current_path+"/results" sub_files = os.listdir(results_path) mAP = [] for i in range(len(sub_files)): class_name = sub_files[i].split(".txt")[0] rec, prec, ap = voc_eval('/data/darknet/results/{}.txt', '/data/darknet/scripts/VOCdevkit/VOC2007/Annotations/{}.xml', '/data/darknet/scripts/VOCdevkit/VOC2007/ImageSets/Main/test.txt', class_name, '.') print("{} :\t {} ".format(class_name, ap)) mAP.append(ap) mAP = tuple(mAP) print("***************************") print("mAP :\t {}".format( float( sum(mAP)/len(mAP)) ))

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