采坑：
No labels in D:\yolov5\train_data\train.cache. Can not train without labels.
參考：https://blog.csdn.net/qq_44787464/article/details/99736670

解決辦法：

STEP1:
一定要按照這個順序：
新建Annotations(存放voc格式的xml）
新建JPEGImages(存放訓(xùn)練的圖片）
新建ImageSets ,labels (這兩個文件為空)
將JPEGImages的圖片復(fù)制到images中

STEP2：
在工程的根目錄下添加makeTxt.py文件,并執(zhí)行
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import os import randomtrainval_percent = 0.1 train_percent = 0.9 xmlfilepath = 'data/Annotations' txtsavepath = 'data/ImageSets' 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('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()

STEP3：

在工程根目錄下新建voc_label.py,并執(zhí)行（注意！！！里面的標(biāo)簽名要改成自己訓(xùn)練標(biāo)簽，否則labels里面的txt文件為空）

import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import joinsets = ['train', 'test','val']################################這里修改為自己的標(biāo)簽名############### classes = ["RBC"]#我們只是檢測細胞，因此只有一個類別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() 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()

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得到labels的具體內(nèi)容以及data目錄下的train.txt，test.txt，val.txt

STEP4：

創(chuàng)建自己yaml文件，在data目錄下：
RBC.yaml

train: /home/zyc/anaconda3/envs/yolov3-master/data/train.txt val: /home/zyc/anaconda3/envs/yolov3-master/data/val.txt test: /home/zyc/anaconda3/envs/yolov3-master/data/test.txt # number of classes nc: 1# class names names: [ 'RBC' ]

最后在train.py
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