YOLOv5知识蒸馏实战篇

YOLOv5s学生网络训练

准备自己的数据集

首先将你的数据集按照标准的voc数据集格式进行如下图的目录树存放

其中Annotations存放的是标注文件，文件格式是xml，JPEGImages存放的是图片。

然后执行下面这个脚本就可以实现以下功能

• 将xml文件格式转换为标准的yolo的txt格式
• 将图片和标注文件划分训练集和验证集

注意将代码中的classes改为你的分类，顺序要对应，并且将convert_annotation函数中的文件路径改为你的对应的路径，路径中不存在中文并且尽量使用绝对路径。

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
 
classes=["ball","messi"]
#classes=["ball"]
 
TRAIN_RATIO = 80
 
def clear_hidden_files(path):
    dir_list = os.listdir(path)
    for i in dir_list:
        abspath = os.path.join(os.path.abspath(path), i)
        if os.path.isfile(abspath):
            if i.startswith("._"):
                os.remove(abspath)
        else:
            clear_hidden_files(abspath)
 
def convert(size, box):
    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)
 
def convert_annotation(image_id):
    in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
    out_file = open('VOCdevkit/VOC2007/YOLOLabels/%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').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))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
    in_file.close()
    out_file.close()
 
wd = os.getcwd()
wd = os.getcwd()
data_base_dir = os.path.join(wd, "VOCdevkit/")
if not os.path.isdir(data_base_dir):
    os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "VOC2007/")
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
if not os.path.isdir(annotation_dir):
        os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
if not os.path.isdir(image_dir):
        os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
if not os.path.isdir(yolo_labels_dir):
        os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(data_base_dir, "images/")
if not os.path.isdir(yolov5_images_dir):
        os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, "labels/")
if not os.path.isdir(yolov5_labels_dir):
        os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")
if not os.path.isdir(yolov5_images_train_dir):
        os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/")
if not os.path.isdir(yolov5_images_test_dir):
        os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")
if not os.path.isdir(yolov5_labels_train_dir):
        os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/")
if not os.path.isdir(yolov5_labels_test_dir):
        os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)
 
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir) # list image files
prob = random.randint(1, 100)
print("Probability: %d" % prob)
for i in range(0,len(list_imgs)):
    path = os.path.join(image_dir,list_imgs[i])
    if os.path.isfile(path):
        image_path = image_dir + list_imgs[i]
        voc_path = list_imgs[i]
        (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
        (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
        annotation_name = nameWithoutExtention + '.xml'
        annotation_path = os.path.join(annotation_dir, annotation_name)
        label_name = nameWithoutExtention + '.txt'
        label_path = os.path.join(yolo_labels_dir, label_name)
    prob = random.randint(1, 100)
    print("Probability: %d" % prob)
    if(prob < TRAIN_RATIO): # train dataset
        if os.path.exists(annotation_path):
            train_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, yolov5_images_train_dir + voc_path)
            copyfile(label_path, yolov5_labels_train_dir + label_name)
    else: # test dataset
        if os.path.exists(annotation_path):
            test_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, yolov5_images_test_dir + voc_path)
            copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()

执行后如下图所示：

 执行完毕以后会生成label文件夹，文件夹下已经划分好训练集和验证集。在yolov5下生成了两个文件yolov5_train.txt和yolov5_val.txt，yolov5_train.txt和yolov5_val.txt分别给出了训练图片文件和验证图片文件的列表， 含有每个图片的路径和文件名。

修改配置文件

创建data/voc_bm.yaml

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ./
train: # train images (relative to 'path')  16551 images
  - VOCdevkit/images/train/ 
val: # val images (relative to 'path')  4952 images
  - VOCdevkit/images/val/
test: # test images (optional)
# Classes
nc: 2  # number of classes
names: ['ball', 'messi']  # class names

 这里的路径要写对，有三种写路径和的方法，主要推荐前面两种，配置文件中的实例就是第一种。

1) dir: path/to/imgs

2) file: path/to/imgs.txt 

val: data/nwpu vhr-10/val.txt                                  #例子
train: data/nwpu vhr-10/train.txt                             #例子
test: data/nwpu vhr-10/test.txt                                #例子

 修改nc类别

然后将model文件夹下面的yolov5s和yolov5m的nc改为2，或者另外新建yolov5svoc和yolov5mvoc。

yolov5s训练自己的数据集

python train.py --data data/voc_bm.yaml --cfg models/yolov5s_bm.yaml --weights weights/yolov5s.pt --batch-size 16 --epochs 100 --workers 4 --name yolov5sbase

 如果开始训练出现长期卡在这里的话

Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...

 是因为项目中缺少这个东西，我直接把这个文件放这里，下载完成以后直接放在总目录下也可以识别

链接：https://pan.baidu.com/s/11AwkBdV0fsavRcRC2EXoOg?pwd=gkgk 
提取码：gkgk

 然后可以正常的开始训练了。

 学生网络训练结果如下：

yolov5m教师网络训练

同上，仅需要将网络模型改为yolov5m就可以，训练命令如下：

python train.py --data data/voc_bm.yaml --cfg models/yolov5m_bm.yaml --weights weights/yolov5m.pt --batch-size 16 --epochs 100 --workers 4 --name yolov5m-base

 教师网络训练结果如下：

知识蒸馏训练

学生网络和教师网络训练完毕进入重点，蒸馏训练

把runs/train/yolov5s-base/weights/best.pt和runs/train/yolov5m-base/weights/best.pt拷贝到weights文件夹下并改名为yolov5szl.pt和yolov5mzl.pt

python train_distillation.py --weights weights/yolov5szl.pt --cfg models/yolov5s_bm.yaml --data data/voc_bm.yaml --batch-size 8 --epochs 100 --workers 4 --t_weights weights/yolov5mzl.pt --hyp data/hyps/hyp.scratch-low-distillation.yaml --distill --dist_loss l2 --name yolov5s-distilled

 训练过程可视化：

结果如下：

对比结果

对比发现这个表格显示了蒸馏L2相对于yolov5s在不同指标下的数值提升。在精度（P）、召回率（R）和平均精度（MAP）方面，蒸馏L2都取得了显著的提升。