YOLOv3：Darknet代码解析（四）结构更改与训练

港控/mmm° 2022-05-18 08:19 529阅读 0赞

**背景：**我们需要降低YOLOv2-tiny的参数量和存储量，以便硬件实现。

**目的：**更改YOLO结构，去掉后面的两层卷积层，降低参数量和运算量。

[YOLOv3：Darknet代码解析（一）安装Darknet][YOLOv3_Darknet_Darknet]

[YOLOv3：Darknet代码解析（二）代码初步][YOLOv3_Darknet]

[YOLOv3：Darknet代码解析（三）卷积操作][YOLOv3_Darknet 1]

[YOLOv3：Darknet代码解析（四）结构更改与训练][YOLOv3_Darknet 2]

[YOLOv3：Darknet代码解析（五）权重与特征存储][YOLOv3_Darknet 3]

[YOLOv3：Darknet代码解析（六）简化的程序与卷积拆分][YOLOv3_Darknet 4]

**目录**

一、更改后的结构

二、训练VOC数据集与COCO数据集

2.1 训练VOC数据集

2.1.1 获得Pascal VOC Data

2.1.2 Generate Labels for VOC

2.1.3 Modify Cfg for Pascal Data

2.1.4 Download Pretrained Convolutional Weights

2.1.5 Train The Model

2.2 Training YOLO on COCO

2.2.1 Get The COCO Data

2.2.2 Modify cfg for COCO

2.2.3 Train The Model

三、在我们的数据集上训练

3.1 用到的一些linux指令

3.1.1 pwd 显示路径

3.1.2 cp复制指令

3.1.3 vi指令，显示文档

3.1.4 重复上条指令

3.2 数据集与cfg

3.3 训练

3.3.1数据集准备

3.3.2 训练

3.3.3 测试

--------------------

# 一、更改后的结构 #

鉴于Yolov2结构相对简单，只有conv和pool。所以直接去掉后面两层卷积，并把最后一层的filter的个数减少为30个。相应的head-hw-v2.cfg文件如下。

[net]
    # Testing
    #batch=1
    #subdivisions=1
    # Training
    batch=64
    subdivisions=8
    width=416
    height=416
    channels=3
    momentum=0.9
    decay=0.0005
    angle=0
    saturation = 1.5
    exposure = 1.5
    hue=.1
    
    learning_rate=0.001
    max_batches = 40200
    policy=steps
    steps=-1,100,20000,30000
    scales=.1,10,.1,.1
    
    [convolutional]
    batch_normalize=1
    filters=16
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=32
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=64
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=128
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=2
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=3
    stride=1
    pad=1
    activation=leaky
    
    [maxpool]
    size=2
    stride=1
    
    [convolutional]
    batch_normalize=1
    filters=1024
    size=3
    stride=1
    pad=1
    activation=leaky
    
    ###########
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=1024
    activation=leaky
    
    [convolutional]
    size=1
    stride=1
    pad=1
    filters=30
    activation=linear
    
    [region]
    anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
    bias_match=1
    classes=1
    coords=4
    num=5
    softmax=1
    jitter=.2
    rescore=1
    
    object_scale=5
    noobject_scale=1
    class_scale=1
    coord_scale=1
    
    absolute=1
    thresh = .6
    random=1

更改结构时注意尽量用mousepad，其他编辑工具容易编辑不当，运行时容易报错。

![70][]

![70 1][]

# 二、训练VOC数据集与COCO数据集 #

## 2.1 训练VOC数据集 ##

### 2.1.1 获得Pascal VOC Data ###

训练YOLO需要2007到2012年的VOC data。 You can find links to the data [here][]. To get all the data, make a directory to store it all and from that directory run:

wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
    wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
    wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
    tar xf VOCtrainval_11-May-2012.tar
    tar xf VOCtrainval_06-Nov-2007.tar
    tar xf VOCtest_06-Nov-2007.tar

There will now be a `VOCdevkit/` subdirectory with all the VOC training data in it.

### 2.1.2 Generate Labels for VOC ###

Now we need to generate the label files that Darknet uses. Darknet wants a `.txt` file for each image with a line for each ground truth object in the image that looks like:

Where `x`, `y`, `width`, and `height` are relative to the image's width and height. To generate these file we will run the `voc_label.py` script in Darknet's `scripts/` directory. Let's just download it again because we are lazy.

wget https://pjreddie.com/media/files/voc_label.py
    python voc_label.py

After a few minutes, this script will generate all of the requisite files. Mostly it generates a lot of label files in `VOCdevkit/VOC2007/labels/` and `VOCdevkit/VOC2012/labels/`. In your directory you should see:

ls
    2007_test.txt   VOCdevkit
    2007_train.txt  voc_label.py
    2007_val.txt    VOCtest_06-Nov-2007.tar
    2012_train.txt  VOCtrainval_06-Nov-2007.tar
    2012_val.txt    VOCtrainval_11-May-2012.tar

The text files like `2007_train.txt` list the image files for that year and image set. Darknet needs one text file with all of the images you want to train on. In this example, let's train with everything except the 2007 test set so that we can test our model. Run:

cat 2007_train.txt 2007_val.txt 2012_*.txt > train.txt

Now we have all the 2007 trainval and the 2012 trainval set in one big list. That's all we have to do for data setup!

### 2.1.3 Modify Cfg for Pascal Data ###

Now go to your Darknet directory. We have to change the `cfg/voc.data` config file to point to your data:

1 classes= 20
      2 train  = <path-to-voc>/train.txt
      3 valid  = <path-to-voc>2007_test.txt
      4 names = data/voc.names
      5 backup = backup

You should replace `<path-to-voc>` with the directory where you put the VOC data.

### 2.1.4 Download Pretrained Convolutional Weights ###

For training we use convolutional weights that are pre-trained on Imagenet. We use weights from the [darknet53][] model. You can just download the weights for the convolutional layers [here (76 MB)][here _76 MB].

wget https://pjreddie.com/media/files/darknet53.conv.74

### 2.1.5 Train The Model ###

Now we can train! Run the command:

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74

## 2.2 Training YOLO on COCO ##

You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets. Here's how to get it working on the [COCO dataset][].

### 2.2.1 Get The COCO Data ###

To train YOLO you will need all of the COCO data and labels. The script `scripts/get_coco_dataset.sh` will do this for you. Figure out where you want to put the COCO data and download it, for example:

cp scripts/get_coco_dataset.sh data
    cd data
    bash get_coco_dataset.sh

Now you should have all the data and the labels generated for Darknet.

### 2.2.2 Modify cfg for COCO ###

Now go to your Darknet directory. We have to change the `cfg/coco.data` config file to point to your data:

1 classes= 80
      2 train  = <path-to-coco>/trainvalno5k.txt
      3 valid  = <path-to-coco>/5k.txt
      4 names = data/coco.names
      5 backup = backup

You should replace `<path-to-coco>` with the directory where you put the COCO data.

You should also modify your model cfg for training instead of testing. `cfg/yolo.cfg` should look like this:

[net]
    # Testing
    # batch=1
    # subdivisions=1
    # Training
    batch=64
    subdivisions=8
    ....

### 2.2.3 Train The Model ###

Now we can train! Run the command:

./darknet detector train cfg/coco.data cfg/yolov3.cfg darknet53.conv.74

If you want to use multiple gpus run:

./darknet detector train cfg/coco.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0,1,2,3

If you want to stop and restart training from a checkpoint:

./darknet detector train cfg/coco.data cfg/yolov3.cfg backup/yolov3.backup -gpus 0,1,2,3

# 三、在我们的数据集上训练 #

## 3.1 用到的一些linux指令 ##

### 3.1.1 pwd 显示路径 ###

### 3.1.2 cp复制指令 ###

[http://man.linuxde.net/cp][http_man.linuxde.net_cp]

cp -r /home/gpu/datasets/lzw/Data/head_data/ /home/gpu/datasets/xxr/darknet/data/

cp -r 整个文件夹复制

以上命令把相应文件整个文件夹拷到我的文件夹中

### 3.1.3 vi指令，显示文档 ###

[http://man.linuxde.net/vi][http_man.linuxde.net_vi]

例如，vi cfg/head.data

esc退出编辑模式进入命令模式，ZZ为存盘并推出，：q为退出

### 3.1.4 重复上条指令 ###

ctrl+p

## 3.2 数据集与cfg ##

./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74

参照voc数据集我们创建cfg/head.data

![70 2][]

分类数为1，其中文件夹中train为相应的路径，valid暂时用不到，head.names文件中改为head，训练好的权重存于backup文件夹中。运用3.1.2整个文件夹复制到相应路径。

## 3.3 训练 ##

### 3.3.1数据集准备 ###

![70 3][]

运用Python的批量写入程序，将准备好的数据集写入train.txt,

"""
    Created on Wed Dec 13, 15:14:53 2017
    批量写入 train.txt
    @auther xingxiangrui
    """        
    import os
    #root = os.getcwd()
    #picpath= '\\0\\1_f\\'
    #filename=os.listdir(root+picpath)
    #for file in filename:
    #    if 'jpg' in file:
    #        print(file)
    openfile=open('train.txt','w')
    
    for root,dirs,files in os.walk(os.getcwd())
        for item in files:
            if 'jpg' in item:
                openfile.write(os.path.join(root,item)+'\n')
            if '.mat' in item or '.m' in item
                os.remove(os.path.join(root,item))
    openfile.close()

### 3.3.2 训练 ###

./darknet detector train cfg/head.data cfg/yolov2-tinyhw.cfg

输入相应的参量，即可训练。

### 3.3.3 测试 ###

./darknet detect cfg/head-hw-v2.cfg headweights/obj_9000.weights data/dog.jpg

[YOLOv3：Darknet代码解析（一）安装Darknet][YOLOv3_Darknet_Darknet]

[YOLOv3：Darknet代码解析（二）代码初步][YOLOv3_Darknet]

[YOLOv3：Darknet代码解析（三）卷积操作][YOLOv3_Darknet 1]

[YOLOv3：Darknet代码解析（四）结构更改与训练][YOLOv3_Darknet 2]

[YOLOv3：Darknet代码解析（五）权重与特征存储][YOLOv3_Darknet 3]

[YOLOv3：Darknet代码解析（六）简化的程序与卷积拆分][YOLOv3_Darknet 4]

[YOLOv3_Darknet_Darknet]: https://blog.csdn.net/weixin_36474809/article/details/80881043
[YOLOv3_Darknet]: https://blog.csdn.net/weixin_36474809/article/details/80975845
[YOLOv3_Darknet 1]: https://blog.csdn.net/weixin_36474809/article/details/81296612
[YOLOv3_Darknet 2]: https://blog.csdn.net/weixin_36474809/article/details/81326286
[YOLOv3_Darknet 3]: https://blog.csdn.net/weixin_36474809/article/details/81331175
[YOLOv3_Darknet 4]: https://blog.csdn.net/weixin_36474809/article/details/81739771
[70]: /images/20220518/193fe5b7c60542c8974aa710023793e8.png
[70 1]: /images/20220518/d0d92c5d562847f7a0424a4f889bb52d.png
[here]: https://pjreddie.com/projects/pascal-voc-dataset-mirror/
[darknet53]: https://pjreddie.com/darknet/imagenet/#darknet53
[here _76 MB]: https://pjreddie.com/media/files/darknet53.conv.74
[COCO dataset]: http://mscoco.org/dataset/#overview
[http_man.linuxde.net_cp]: http://man.linuxde.net/cp
[http_man.linuxde.net_vi]: http://man.linuxde.net/vi
[70 2]: /images/20220518/fda6402cd3e64c80832f061c25c5add7.png
[70 3]: /images/20220518/78d539bd9f704405a23e266ac408d477.png