前提准备条件：
已安装好了的pycharm,pytorch，yolov5
准备数据集
https://swfscdata.nmfs.noaa.gov/labeled-fishes-in-the-wild/下载好，因为是第一次做于是只选取其中的63张图片
打开软件精灵标记助手，进行标注后导出
在yolov5/data下新建项目annotations(存放xml文件)，imageSets空项目，images(存放原始图片)（原本不是这个名字，但代码中读取图片用的是images，于是懒得改代码，就把自己的项目名称改了）
import os
import random
trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = 'data/Annotations'
txtsavepath = 'data/ImageSets'
total_xml = os.listdir(xmlfilepath)#xml文件路径
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()在imageSets文件夹中生成四个txt文件
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 = ['鱼']
def convert(size, box):# xml坐标转化为yolo坐标
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)#返回中心点坐标以及宽度和高度
def convert_annotation(image_id):#将annotation下的xml文件转化为label的.txt文件
in_file = open('data/Annotations/%s.xml' % (image_id))
out_file = open('data/labels/%s.txt' % (image_id), 'w')
tree = ET.parse(in_file)#打开xml文件
root = tree.getroot()#得到xml文件对应的根节点
size = root.find('size')
w = int(size.find('width').text)#图片的宽
h = int(size.find('height').text)#图片的高
for obj in root.iter('object'):#遍历所有框
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))
b1, b2, b3, b4 = b
# 标注越界修正
if b2 > w:
b2 = w
if b4 > h:
b4 = h
b = (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()
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()
值得一提的是，注意代码中的classes与标注时的类名必须完全一致，我第一次就是因为一个是鱼一个是fish而在labels中得到一堆空文件
最终labels文件中得到图片对应的txt,在data目录下生成了train.txt,test.txt,val.txt
经常遇到.cache文件，删了即可
在data目录下新建文件fish.yaml
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: data/train.txt # 128 images
val: data/val.txt
#test：data/test.txt
# number of classes
nc: 1
# class names
names: [ '鱼' ]在models文件夹中修改使用的预配重文件（类别nc=1）
最后修改train.py
parser.add_argument('--weights', type=str, default='data/scripts/weights/yolov5s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default= 'data/fish.yaml', help='dataset.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
parser.add_argument('--batch-size', type=int, default=4, help='total batch size for all GPUs, -1 for autobatch')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
parser.add_argument('--noplots', action='store_true', help='save no plot files')
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')相关参数可见
http://www.tlcement.com/35410.html
在用detact检测时
在data文件夹里放了sss检测文件夹
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp8/weights/best.pt', help='model path or triton URL')
parser.add_argument('--source', type=str, default='data/sss/', help='file/dir/URL/glob/screen/0(webcam)')
parser.add_argument('--data', type=str, default='data/fish.yaml', help='(optional) dataset.yaml path')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='show results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')参考文章
https://blog.csdn.net/weixin_44145782/article/details/113983421
https://blog.csdn.net/weixin_48270248/article/details/112600563