目标检测|目标检测笔记(四)（YOLO-V4-Tiny 源码训练、测试、验证详细步骤） pytorch|python

Yolov4-Tiny

- 下载源码和权重文件
- 编译环境
- 简单测试
- 训练VOC数据集
- 生成训练文件
- 训练准备
- 开始训练
- 多GPU训练

下载源码和权重文件源码：https://github.com/AlexeyAB/darknet
权重：https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights
编译环境修改makefile(打开darknet目录下makefile文件),
根据具体情况修改

GPU=1# 使用GPU CUDNN=1# 使用GPU CUDNN_HALF=1# 混合精度训练，用于加速 OPENCV=1# 使用opencv AVX=0 OPENMP=0 LIBSO=1# 生成libdarknet.so，便于python调用darknet模型 ZED_CAMERA=0 ZED_CAMERA_v2_8=0#ARCH= -gencode arch=compute_35,code=sm_35 \ #-gencode arch=compute_50,code=[sm_50,compute_50] \ #-gencode arch=compute_52,code=[sm_52,compute_52] \ #-gencode arch=compute_61,code=[sm_61,compute_61]OS := $(shell uname)# GeForce RTX 3070, 3080, 3090 # ARCH= -gencode arch=compute_86,code=[sm_86,compute_86]# Kepler GeForce GTX 770, GTX 760, GT 740 # ARCH= -gencode arch=compute_30,code=sm_30# Tesla A100 (GA100), DGX-A100, RTX 3080 ARCH= -gencode arch=compute_80,code=[sm_80,compute_80]# Tesla V100 # ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]

NVCC=/usr/local/cuda-11.1/bin/nvcc

然后直接终端进行编译

sudo make

就会在当前文件夹生成libdarknet.so文件。
简单测试

./darknet detector test cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights data/dog.jpg # 图片测试 ./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -ext_output test.mp4# 视频测试 ./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -c 0# 摄像头测试

训练VOC数据集 【目标检测|目标检测笔记(四)（YOLO-V4-Tiny 源码训练、测试、验证详细步骤）】通过下面代码划分，创建一个data_spilt.py文件和对应的路径

import os import randomtrainval_percent = 0.8# 所有数据中用来训练的比例 trainval/alltrainval=train+val train_percent = 1# trainval用来训练的比例 train/trainval xmlfilepath = '/home/lqs/Downloads/dataset/VOC/VOC2007/Annotations' txtsavepath = './VOCdevkit/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(txtsavepath+'/trainval.txt', 'w+') # 训练集数据+验证集数据 ftest = open(txtsavepath+'/test.txt', 'w+') # 测试集 ftrain = open(txtsavepath+'/train.txt', 'w+') # 训练集 fval = open(txtsavepath+'/val.txt', 'w+') # 验证集for iin 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() print('Finished!')

生成训练文件修改sets和classes以及里面的关键路径即可，这个代码可自己创建，得到2007_test.txt、2007_train.txt、2007_val.txt三个文件

#---------------------------------------------# #运行前一定要修改classes #如果生成的2007_train.txt里面没有目标信息 #那么就是因为classes没有设定正确 #---------------------------------------------# import xml.etree.ElementTree as ET from os import getcwdsets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')] #-----------------------------------------------------# #这里设定的classes顺序要和model_data里的txt一样 #-----------------------------------------------------# classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]def convert_annotation(year, image_id, list_file): in_file = open('/home/lqs/Downloads/dataset/VOC/VOC%s/Annotations/%s.xml'%(year, image_id), encoding='utf-8') tree=ET.parse(in_file) root = tree.getroot()for obj in root.iter('object'): difficult = 0 if obj.find('difficult')!=None: 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 = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text))) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))wd = getcwd()for year, image_set in sets: image_ids = open('/home/lqs/Downloads/dataset/VOC/VOC%s/ImageSets/Main/%s.txt'%(year, image_set), encoding='utf-8').read().strip().split() list_file = open('%s_%s.txt'%(year, image_set), 'w', encoding='utf-8') for image_id in image_ids: list_file.write('/home/lqs/Downloads/dataset/VOC%s/JPEGImages/%s.jpg'%(year, image_id)) convert_annotation(year, image_id, list_file) list_file.write('\n') list_file.close()

训练准备

修改darknet-master/cfg/voc.data

classes= 20# 改成自己的类别数 train= /home/pjreddie/data/2007_train.txt # 改成voc_label.py生成的2007_train.txt路径 valid= /home/pjreddie/data/2007_test.txt # 改成voc_label.py生成的2007_test.txt路径 names = data/voc.names # 改成有自己类别的names文件 backup = backup/ # 改为backup/即可

修改darknet-master/cfg/yolov4-tiny.cfg

[net] # Testing #batch=1 #subdivisions=1 # Training batch=64 # 每64张图片更新一次参数 subdivisions=16 # 64张图片分16次放入显卡中，每次4张。因为一次放入过多会内存不足，根据自己显卡性能更改 width=416 # 将输入的图片resize到width×height后，放入网络中训练 height=416 # 只要是32的倍数即可 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1

找到如下位置（以yolo为关键字），修改filters和classes，整个文本共有2个filters和2个classes需要修改

[convolutional] size=1 stride=1 pad=1 filters=255 # 改为3*(classes +5)。不过官网上不建议这种做法，具体可以查官方github 修改地方不止一处！ activation=linear[yolo] mask = 3,4,5 anchors = 10,14,23,27,37,58,81,82,135,169,344,319 classes=80 # 改为自己的类别数 num=6 jitter=.3 scale_x_y = 1.05 cls_normalizer=1.0 iou_normalizer=0.07

开始训练在darknet目录下，使用第一条命令生成预训练权重yolov4-tiny.conv.29，第二条命令开始训练

./darknet partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29 # 生成yolov4-tiny.conv.29文件，用于迁移学习 ./darknet detector train cfg/voc.data cfg/yolov4-tiny.cfg yolov4-tiny.conv.29 -map # 训练模型 # -map参数可以在训练过程中对测试集计算map并在chart上显示)

Error in load_data_detection() - OpenCV
多GPU训练