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Paddlepaddle使用自己的VOC数据集训练目标检测（0废话简易教程）

news 2025/6/27 0:17:38

一安装paddlepaddle和paddledection（略）

笔者使用的是自己的数据集

二在dataset目录下新建自己的数据集文件，如下：

其中

xml文件内容如下：

另外新建一个createList.py文件：

# -- coding: UTF-8 --
import os
import os.path as osp
import re
import randomdevkit_dir = '../smoke/'
years = ['2007', '2012']def get_dir(devkit_dir,  type):return osp.join(devkit_dir, type)def walk_dir(devkit_dir):filelist_dir = get_dir(devkit_dir, 'ImageSets/Main')annotation_dir = get_dir(devkit_dir, 'annotations')img_dir = get_dir(devkit_dir, 'images')trainval_list = []test_list = []added = set()for _, _, files in os.walk(filelist_dir):for fname in files:img_ann_list = []if re.match('train\.txt', fname):img_ann_list = trainval_listelif re.match('val\.txt', fname):img_ann_list = test_listelse:continuefpath = osp.join(filelist_dir, fname)for line in open(fpath):name_prefix = line.strip().split()[0]if name_prefix in added:continueadded.add(name_prefix)ann_path = osp.join(annotation_dir, name_prefix + '.xml')img_path = osp.join(img_dir, name_prefix + '.jpg')assert os.path.isfile(ann_path), 'file %s not found.' % ann_pathassert os.path.isfile(img_path), 'file %s not found.' % img_pathimg_ann_list.append((img_path, ann_path))return trainval_list, test_listdef prepare_filelist(devkit_dir, output_dir):trainval_list = []test_list = []trainval, test = walk_dir(devkit_dir)trainval_list.extend(trainval)test_list.extend(test)random.shuffle(trainval_list)with open(osp.join(output_dir, 'trainval.txt'), 'w') as ftrainval:for item in trainval_list:ftrainval.write(item[0] + ' ' + item[1] + '\n')with open(osp.join(output_dir, 'test.txt'), 'w') as ftest:for item in test_list:ftest.write(item[0] + ' ' + item[1] + '\n')if __name__ == '__main__':prepare_filelist(devkit_dir, '../smoke')

一个data2tarin.py文件：

# -- coding: UTF-8 --
import os
import randomtrainval_percent = 0.9
train_percent = 0.9
xml = r"D:\Coding\PaddleDetection-release-2.7\dataset\smoke\annotations"
save_path = r"D:\Coding\PaddleDetection-release-2.7\dataset\smoke\ImageSets\Main"if not os.path.exists(save_path):os.makedirs(save_path)total_xml = os.listdir(xml)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("traub size", tr)
ftrainval = open(os.path.join(save_path, 'trainval.txt'), 'w')
ftest = open(os.path.join(save_path, 'test.txt'), 'w')
ftrain = open(os.path.join(save_path, 'train.txt'), 'w')
fval = open(os.path.join(save_path, '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()

运行以上两个脚本，结果如图：

新建label_list.txt文件，内容如下，为标签文件：

三新建smoke.yml文件

内容如下：

metric: VOC
map_type: 11point
num_classes: 4TrainDataset:name: VOCDataSetdataset_dir: dataset/smokeanno_path: trainval.txtlabel_list: label_list.txtdata_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']EvalDataset:name: VOCDataSetdataset_dir: dataset/smokeanno_path: test.txtlabel_list: label_list.txtdata_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']TestDataset:name: ImageFolderanno_path: dataset/smoke/label_list.txt

主要修改num_classes以及dataset_dir和anno_path