本文记录一下，使用FSDet进行小样本目标检测的一个实践过程（以铝材瑕疵数据为例）。

数据准备

数据来源

使用的数据集为 [飞粤云端2018]广东工业智造大数据创新大赛—智能算法赛 的数据，百度云链接: https://pan.baidu.com/s/1sncD_D12PkR4D8huei3d6Q 提取码: egwc

使用的数据为赛制第二阶段的数据，数据格式为：

在每个类别中随机挑选了15张-20张图片组成小样本数据集。

数据预处理

1. 将文件中的中文改为英文

# -*- coding=utf-8-*-"""@Time   : 22-7-12下午4:49@Author : MaQian@language : Python2.7
"""
import os
import numpy as np
import codecs
import json
from glob import glob
import cv2
import shutil
from xml.dom import minidom
import xml.etree.cElementTree as ET
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedShuffleSplit# 将中文名字改为英文名  瑕疵名字用拼音代替
def change_cn2en():cn2en_names = {'不导电': 'BuDaoDian', '喷流': 'PenLiu', '擦花': 'CaHua', '杂色': 'ZaSe','桔皮': 'JuPi', '漆泡': 'QiPao', '漏底': 'LouDi', '脏点': 'ZangDian','角位漏底': 'JiaoWeiLouDi', '起坑': 'QiKeng', '正常': 'ZhengChang'}path = './few-shot-lvcai-data'imgdir = os.listdir(path)for id in imgdir:file_path = os.path.join(path, id)# print id.decode(encoding='utf-8')# print(file_path)file_names = os.listdir(file_path)for file_name in file_names:# print(file_name)name = file_name.strip().split('.')[0]type = file_name.strip().split('.')[1]xiaciname = name[0:name.index('2018')]if xiaciname.__contains__(','):xcnames = xiaciname.strip().split(',')for xcn in xcnames:name = name.replace(xcn, cn2en_names[xcn])name = name.replace(',', "And")else:name = name.replace(xiaciname, cn2en_names[xiaciname])name = name[0:name.index('对照')]name = name + '.' + type# print(name)# os.rename(file_name, name)os.rename(os.path.join(file_path, file_name), os.path.join(file_path, name))# 将文件夹也改为英文
def change_cn2en2():cn2en_names = {'不导电': 'BuDaoDian', '喷流': 'PenLiu', '擦花': 'CaHua', '杂色': 'ZaSe','桔皮': 'JuPi', '漆泡': 'QiPao', '漏底': 'LouDi', '脏点': 'ZangDian','角位漏底': 'JiaoWeiLouDi', '起坑': 'QiKeng', '正常': 'ZhengChang'}path = './few-shot-lvcai-data'imgdir = os.listdir(path)for id in imgdir:if len(id) < 5:# print(id, cn2en_names[id])os.rename(os.path.join(path, id), os.path.join(path, cn2en_names[id]))

1. 将json文件转换为xml文件

# 将数据集转换为VOC格式# 处理json文件，生成xml文件
def handel_json2xml():label_warp = {'不导电': 'BuDaoDian','喷流': 'PenLiu','擦花': 'CaHua','杂色': 'ZaSe','桔皮': 'JuPi','漆泡': 'QiPao','漏底': 'LouDi','脏点': 'ZangDian','角位漏底': 'JiaoWeiLouDi','起坑': 'QiKeng'}# 保存路径saved_path = "./VOC2007/"# 创建要求文件夹if not os.path.exists(saved_path + "Annotations"):os.makedirs(saved_path + "Annotations")if not os.path.exists(saved_path + "JPEGImages/"):os.makedirs(saved_path + "JPEGImages/")if not os.path.exists(saved_path + "ImageSets/Main/"):os.makedirs(saved_path + "ImageSets/Main/")src_path = './few-shot-lvcai-data'src_filepath = os.listdir(src_path)json_paths = []# image_paths = []for id in src_filepath:if not id.__contains__('图片'):file_path1 = os.path.join(src_path, id)file_names = os.listdir(file_path1)for file_name in file_names:if file_name.endswith('json'):json_paths.append(os.path.join(file_path1, file_name))# image_paths.append(os.path.join(file_path1, file_name.strip().split('.json')[0]+'.jpg'))# print(len(json_paths))# print(len(image_paths))# 读取标注信息并写入 xmlfor json_file_path in json_paths:json_file_name = json_file_path.strip().split('.json')[0].split('/')[3]json_file = json.load(open(json_file_path, "r"))img_file_path = json_file_path.strip().split('.json')[0] + '.jpg'# print(img_file_path)height, width, channels = cv2.imread(img_file_path).shapewith codecs.open(saved_path + "Annotations/" + json_file_name + ".xml", "w", "utf-8") as xml:xml.write('<annotation>\n')xml.write('\t<folder>' + 'LvCai_data' + '</folder>\n')xml.write('\t<filename>' + json_file_name + ".jpg" + '</filename>\n')xml.write('\t<source>\n')xml.write('\t\t<database>LvCai Data</database>\n')xml.write('\t\t<annotation>LvCai</annotation>\n')xml.write('\t\t<image>flickr</image>\n')xml.write('\t\t<flickrid>NULL</flickrid>\n')xml.write('\t</source>\n')xml.write('\t<owner>\n')xml.write('\t\t<flickrid>NULL</flickrid>\n')xml.write('\t\t<name>LvCai</name>\n')xml.write('\t</owner>\n')xml.write('\t<size>\n')xml.write('\t\t<width>' + str(width) + '</width>\n')xml.write('\t\t<height>' + str(height) + '</height>\n')xml.write('\t\t<depth>' + str(channels) + '</depth>\n')xml.write('\t</size>\n')xml.write('\t\t<segmented>0</segmented>\n')for multi in json_file["shapes"]:points = np.array(multi["points"])labelName = multi["label"]# 此处注意，根据自己的数据集判断是否需要－１操作xmin = min(points[:, 0])  # -1xmax = max(points[:, 0])  # -1ymin = min(points[:, 1])  # -1ymax = max(points[:, 1])  # -1# label = multi["label"]label = label_warp[multi["label"]]if xmax <= xmin:passelif ymax <= ymin:passelse:xml.write('\t<object>\n')xml.write('\t\t<name>' + label + '</name>\n')xml.write('\t\t<pose>Unspecified</pose>\n')xml.write('\t\t<truncated>1</truncated>\n')xml.write('\t\t<difficult>0</difficult>\n')xml.write('\t\t<bndbox>\n')xml.write('\t\t\t<xmin>' + str(int(xmin)) + '</xmin>\n')xml.write('\t\t\t<ymin>' + str(int(ymin)) + '</ymin>\n')xml.write('\t\t\t<xmax>' + str(int(xmax)) + '</xmax>\n')xml.write('\t\t\t<ymax>' + str(int(ymax)) + '</ymax>\n')xml.write('\t\t</bndbox>\n')xml.write('\t</object>\n')# print(json_file_name, xmin, ymin, xmax, ymax, label)xml.write('</annotation>')

1. 将所有jpg图片移动到JPEGImages/路径下

# 复制图片到 JPEGImages/下
def move_img():# 保存路径saved_path = "./VOC2007/"src_path = './few-shot-lvcai-data'src_filepath = os.listdir(src_path)image_paths = []for id in src_filepath:if not id.__contains__('图片'):file_path1 = os.path.join(src_path, id)file_names = os.listdir(file_path1)for file_name in file_names:if file_name.endswith('jpg'):image_paths.append(os.path.join(file_path1, file_name))# print(len(image_paths))# image_files = glob(image_paths)print("copy image files to $DataPath/JPEGImages/")for image in image_paths:shutil.copy(image, saved_path + "JPEGImages/")

1. 划分train和test集合
   此处粘贴两份代码，第一份是使用随机抽样，第二份是使用分层抽样。
   因为本文是小样本目标检测，数据量较少，如果使用随机抽样会导致test集合中种类覆盖不全，而分层抽样可以覆盖完全。

• 随机抽样

def split_train_test():# 保存路径saved_path = "./few-shot-lvcai-data-voc/"txtsavepath = saved_path + "ImageSets/Main/"ftrainval = open(txtsavepath + '/trainval.txt', 'w')ftest = open(txtsavepath + '/test.txt', 'w')ftrain = open(txtsavepath + '/train.txt', 'w')fval = open(txtsavepath + '/val.txt', 'w')total_files = glob(saved_path + "/Annotations/*.xml")total_files = [i.replace("\\", "/").split("/")[-1].split(".xml")[0] for i in total_files]isUseTest = Trueif isUseTest:trainval_files, test_files = train_test_split(total_files, test_size=0.15, random_state=55)else:trainval_files = total_filesfor file in trainval_files:ftrainval.write(file + "\n")# splittrain_files, val_files = train_test_split(trainval_files, test_size=0.15, random_state=55)# trainfor file in train_files:ftrain.write(file + "\n")# valfor file in val_files:fval.write(file + "\n")for file in test_files:print(file)ftest.write(file + "\n")ftrainval.close()ftrain.close()fval.close()ftest.close()

• 分层抽样

def split_train_test():# 保存路径saved_path = "./VOC2007/"txtsavepath = saved_path + "ImageSets/Main/"ftrainval = open(txtsavepath + '/trainval.txt', 'w')ftest = open(txtsavepath + '/test.txt', 'w')ftrain = open(txtsavepath + '/train.txt', 'w')fval = open(txtsavepath + '/val.txt', 'w')total_files = glob(saved_path + "Annotations/*.xml")# print(total_files)classes = ['BuDaoDian', 'PenLiu', 'CaHua','ZaSe', 'JuPi', 'QiPao', 'LouDi','ZangDian', 'JiaoWeiLouDi', 'QiKeng']x = []y = []for file in total_files:file_name = file.strip().split('.xml')[0].split('/')[-1]xiaci_name = file_name[0:file_name.index('2018')]x.append(file_name)y.append(classes.index(xiaci_name))# 划分train和test集合# 因为数据量较少，直接随机划分会导致test集合无法包含所有的瑕疵类别ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=20)tt = ss.split(x, y)trainval_files = []test_files = []for train_index, test_index in tt:for i in train_index:trainval_files.append(x[i])for j in test_index:test_files.append(x[j])train_files, val_files = train_test_split(trainval_files, test_size=0.15, random_state=55)# trainvalfor file in trainval_files:ftrainval.write(file + "\n")# trainfor file in train_files:ftrain.write(file + "\n")# valfor file in val_files:fval.write(file + "\n")# testfor file in test_files:# print(file)ftest.write(file + "\n")ftrainval.close()ftrain.close()fval.close()ftest.close()

1. 将信息写入(class_name)_test.txt、(class_name)_train.txt、(class_name)_val.txt、(class_name)_trainval.txt

# 将信息写入(class_name)_test.txt、(class_name)_train.txt、(class_name)_val.txt、(class_name)_trainval.txt
def per_class_trainval_test():Classes_name = ['BuDaoDian', 'PenLiu', 'CaHua', 'ZaSe', 'JuPi', 'QiPao', 'LouDi', 'ZangDian','JiaoWeiLouDi', 'QiKeng', 'ZhengChang']# 保存路径saved_path = "./VOC2007/"txtsavepath = saved_path + "ImageSets/Main/"ftrainval = open(txtsavepath + '/trainval.txt', 'r')ftest = open(txtsavepath + '/test.txt', 'r')ftrain = open(txtsavepath + '/train.txt', 'r')fval = open(txtsavepath + '/val.txt', 'r')trainval = ftrainval.readlines()test = ftest.readlines()train = ftrain.readlines()val = fval.readlines()# print(trainval)# print('ZangDian20180831094211' in trainval)xml_file_path = saved_path + "/Annotations"total_xml = os.listdir(xml_file_path)# print(len(total_xml))for idx in range(len(Classes_name)):  # 每一个类单独处理class_name = Classes_name[idx]# print('class_name:', class_name)# 创建txtclass_trainval = open(os.path.join(saved_path + 'ImageSets/Main', str(class_name) + '_trainval.txt'), 'w')class_test = open(os.path.join(saved_path + 'ImageSets/Main', str(class_name) + '_test.txt'), 'w')class_train = open(os.path.join(saved_path + 'ImageSets/Main', str(class_name) + '_train.txt'), 'w')class_val = open(os.path.join(saved_path + 'ImageSets/Main', str(class_name) + '_val.txt'), 'w')for file in os.listdir(xml_file_path):# print('file:', file)file_name = file.strip().split('.')[0]tree = ET.parse(os.path.join(xml_file_path, file))root = tree.getroot()for obj in root.findall('object'):name = obj.find('name').text# print('name:', name)if class_name == name:flag = 1else:flag = -1if file_name + '\n' in trainval:class_trainval.write(file_name + ' ' + str(flag) + "\n")if file_name + '\n' in train:class_train.write(file_name + ' ' + str(flag) + "\n")else:class_val.write(file_name + ' ' + str(flag) + "\n")else:class_test.write(file_name + ' ' + str(flag) + "\n")# print('==' * 20)class_trainval.close()class_test.close()class_train.close()class_val.close()

以上文件放置在FSDet/datasets/路径下。如图所示：（其中vocsplit文件在后面的章节中再生成。）

FSDet

代码地址：https://github.com/wz940216/few-shot-object-detection#data-preparation
另外还有一个参考博客： https://blog.csdn.net/qq_35030874/article/details/116996814

step1.配置环境，跑通demo.py

这一步按照github中的步骤，可以比较容易的完成。

step2.准备base model

我下载的是"voc/split1/base_model/model_final.pth"作为基础模型，放置在./base_model文件夹下。

执行以下命令，会在save_dir下生成model_reset_surgery.pth文件。

python -m tools.ckpt_surgery --src1 ./base_model/model_final_base.pth --method randinit --save-dir checkpoints/coco/faster_rcnn/faster_rcnn_R_50_FPN_base

step3.制作自己的few-shot数据集

主要需要修改的是这几个代码。

我的数据集是铝材数据，共有10种瑕疵。

builtin.py

修改register_all_pascal_voc（）方法

builtin_meta.py

修改为自己数据集的类别

meta_pascal_voc.py

1.该文件主要修改的就是路径，和自己数据集的路径对应即可。

2.注意此处：

prepare_voc_few_shot.py

修改为自己的数据集类别

注意修改路径

python prepare_voc_few_shot.py

运行该文件，则会在vocsplit路径下生成如下图所示的文件。

step4.修改配置文件

1shot对应的配置文件为：faster_rcnn_R_101_FPN_ft_all1_1shot.yaml

其中，两个路径和自己的路径对应，
TRAIN填写自己想要训练的数据所在文件夹，例：

'voc_2007_trainval_novel1_1shot','voc_2007_trainval_novel1_1shot_seed1','voc_2007_trainval_novel1_1shot_seed2','voc_2007_trainval_novel1_1shot_seed3','voc_2007_trainval_novel1_1shot_seed4','voc_2007_trainval_novel1_1shot_seed5','voc_2007_trainval_novel1_1shot_seed6','voc_2007_trainval_novel1_1shot_seed7','voc_2007_trainval_novel1_1shot_seed8','voc_2007_trainval_novel1_1shot_seed9','voc_2007_trainval_novel1_1shot_seed10','voc_2007_trainval_novel1_1shot_seed11','voc_2007_trainval_novel1_1shot_seed12','voc_2007_trainval_novel1_1shot_seed13','voc_2007_trainval_novel1_1shot_seed14','voc_2007_trainval_novel1_1shot_seed15','voc_2007_trainval_novel1_1shot_seed16','voc_2007_trainval_novel1_1shot_seed17','voc_2007_trainval_novel1_1shot_seed18','voc_2007_trainval_novel1_1shot_seed19'

step5：运行

1shot

运行命令为：

python3 -m tools.train_net --num-gpus 2  --config-file configs/PascalVOC-detection/split1/faster_rcnn_R_101_FPN_ft_all1_1shot.yaml         --opts MODEL.WEIGHTS ./checkpoints/voc/faster_rcnn/faster_rcnn_R_50_FPN_base/model_reset_surgery.pth

• 测试结果

[07/19 12:26:56 fsdet.evaluation.pascal_voc_evaluation]: Evaluate per-class mAP50:

BuDaoDian	PenLiu	CaHua	ZaSe	JuPi	QiPao	LouDi	ZangDian	JiaoWeiLouDi	QiKeng
0.000	0.000	0.000	100.000	0.000	0.000	0.000	9.091	0.000	0.000

[07/19 12:26:56 fsdet.evaluation.pascal_voc_evaluation]: Evaluate overall bbox:

AP	AP50	AP75	nAP	nAP50	nAP75
8.068	10.909	10.909	8.068	10.909	10.909

2shot

2shot对应的配置文件为：faster_rcnn_R_101_FPN_ft_all1_2shot.yaml
也是相应的修改以上三个地方，运行命令中改为2shot即可。
运行命令为：

python3 -m tools.train_net --num-gpus 2  --config-file configs/PascalVOC-detection/split1/faster_rcnn_R_101_FPN_ft_all1_2shot.yaml         --opts MODEL.WEIGHTS ./checkpoints/voc/faster_rcnn/faster_rcnn_R_50_FPN_base/model_reset_surgery.pth

• 测试结果

[07/19 15:25:58 fsdet.evaluation.pascal_voc_evaluation]: Evaluate per-class mAP50:

BuDaoDian	PenLiu	CaHua	ZaSe	JuPi	QiPao	LouDi	ZangDian	JiaoWeiLouDi	QiKeng
0.000	0.000	0.000	100.000	2.020	0.000	0.000	27.273	0.000	0.000

[07/19 15:25:58 fsdet.evaluation.pascal_voc_evaluation]: Evaluate overall bbox:

AP	AP50	AP75	nAP	nAP50	nAP75
10.697	12.929	12.727	10.697	12.929	12.727

3shot

运行命令：

python3 -m tools.train_net --num-gpus 2
--config-file configs/PascalVOC-detection/split1/faster_rcnn_R_101_FPN_ft_all1_3shot.yaml         --opts MODEL.WEIGHTS ./checkpoints/voc/faster_rcnn/faster_rcnn_R_50_FPN_base/model_reset_surgery.pth

• 测试结果
  [07/19 18:21:31 fsdet.evaluation.pascal_voc_evaluation]: Evaluate per-class mAP50:

BuDaoDian	PenLiu	CaHua	ZaSe	JuPi	QiPao	LouDi	ZangDian	JiaoWeiLouDi	QiKeng
0.000	0.000	0.000	100.000	2.797	0.000	0.000	36.364	0.000	0.000

[07/19 18:21:31 fsdet.evaluation.pascal_voc_evaluation]: Evaluate overall bbox:

AP	AP50	AP75	nAP	nAP50	nAP75
11.629	13.916	13.636	11.629	13.916	13.636

5shot

运行命令：

python3 -m tools.train_net --num-gpus 2 --config-file configs/PascalVOC-detection/split1/faster_rcnn_R_101_FPN_ft_all1_5shot.yaml         --opts MODEL.WEIGHTS ./checkpoints/voc/faster_rcnn/faster_rcnn_R_50_FPN_base/model_reset_surgery.pth

• 测试结果

[07/20 14:08:23 fsdet.evaluation.pascal_voc_evaluation]: Evaluate per-class mAP50:

BuDaoDian	PenLiu	CaHua	ZaSe	JuPi	QiPao	LouDi	ZangDian	JiaoWeiLouDi	QiKeng
0.000	0.000	0.000	100.000	12.121	0.000	0.000	36.364	0.000	0.000

[07/20 14:08:23 fsdet.evaluation.pascal_voc_evaluation]: Evaluate overall bbox:

AP	AP50	AP75	nAP	nAP50	nAP75
12.394	14.848	14.848	12.394	14.848	14.848

7shot

运行命令：

python3 -m tools.train_net --num-gpus 2--config-file configs/PascalVOC-detection/split1/faster_rcnn_R_101_FPN_ft_all1_7shot.yaml--opts MODEL.WEIGHTS ./checkpoints/voc/faster_rcnn/faster_rcnn_R_50_FPN_base/model_reset_surgery.pth

• 测试结果

[07/20 18:57:56 fsdet.evaluation.pascal_voc_evaluation]: Evaluate per-class mAP50:

BuDaoDian	PenLiu	CaHua	ZaSe	JuPi	QiPao	LouDi	ZangDian	JiaoWeiLouDi	QiKeng
0.000	0.000	0.000	100.000	12.121	0.000	0.000	36.364	0.000	0.000

[07/20 18:57:56 fsdet.evaluation.pascal_voc_evaluation]: Evaluate overall bbox:

AP	AP50	AP75	nAP	nAP50	nAP75
12.273	14.848	14.545	12.273	14.848	14.545

从上述结果可以看出，效果并不好，多个类别的AP为0,，在尝试了各种方法，还是没有改善之后，看到了这个issue.从这个issue中的描述，我们将配置文件中的 BACKBONE的FREEZE改为 False之后，得到的结果如下所示：

[07/25 12:06:00 fsdet.evaluation.pascal_voc_evaluation]: Evaluate per-class mAP50:

BuDaoDian	PenLiu	CaHua	ZaSe	JuPi	QiPao	LouDi	ZangDian	JiaoWeiLouDi	QiKeng
100.000	72.727	45.455	100.000	100.000	63.636	100.000	72.727	100.000	100.000

[07/25 12:06:00 fsdet.evaluation.pascal_voc_evaluation]: Evaluate overall bbox:

AP	AP50	AP75	nAP	nAP50	nAP75
62.803	85.455	67.273	62.803	85.455	67.273

效果有明显提升，至于具体的原因，目前还未找出。

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