憨批的语义分割3——unet模型详解以及训练自己的unet模型（划分斑马线）

忘是亡心i 2023-05-30 09:18 22阅读 0赞

### 憨批的语义分割3——unet模型详解以及训练自己的unet模型（划分斑马线） ###

*  学习前言
 *  模型部分
 *   *  什么是unet模型
     *  unet模型的代码实现
     *   *  1、主干模型Mobilenet。
         *  2、unet的Decoder解码部分
     *  代码测试
 *  训练部分
 *   *  训练的是什么
     *   *  1、训练文件详解
         *  2、LOSS函数的组成
     *  训练代码
     *   *  1、文件存放方式
         *  2、训练文件
         *  3、预测文件
     *  训练结果

# 学习前言 #

在这一个BLOG里，我会跟大家讲一下什么是unet模型，以及如何训练自己的unet模型，其训练与上一篇的segnet模型差距不大，但是结构上有一定的差距。如果想要先有语义分割的基础，可以看我的博文[憨批的语义分割2——训练自己的segnet模型（划分斑马线）][2_segnet]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]

# 模型部分 #

## 什么是unet模型 ##

unet是一个语义分割模型，其主要执行过程与其它语义分割模型类似，**首先利用卷积进行下采样，然后提取出一层又一层的特征，利用这一层又一层的特征，其再进行上采样，最后得出一个每个像素点对应其种类的图像。**

看如下这幅图我们大概可以看出个所以然来：  
在进行segnet的详解的时候我们知道，**其只选了一个hw压缩了四次的特征层进行三次上采样得到最后的结果**。  
但是unet不一样，**其利用到了压缩了二、三、四次的特征层，最后输出图像分割的结果（可以选择是否需要压缩了一次的特征层）。**![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 1]  
具体的网络结构如下，左边的顺序从上向下传播，右边的顺序从下向上传播：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 2]  
其主要的过程就是，**将hw被压缩了四次的f4进行一次上采样后与f3进行concatenate，然后再进行一次上采样与f2进行concatenate，然后再进行一次上采样（这里可以选择是否与f1进行concatenate），最后利用卷积输出filter为nclasses的图像。（一共进行三次上采样）**

## unet模型的代码实现 ##

unet模型的代码分为两部分。

### 1、主干模型Mobilenet。 ###

该部分用于特征提取，实际上就是常规的mobilenet结构，想要了解mobilenet结构的朋友们可以看看我的另一篇博客[神经网络学习小记录23——MobileNet模型的复现详解][23_MobileNet]：

from keras.models import *
    from keras.layers import *
    import keras.backend as K
    import keras
    
    IMAGE_ORDERING = 'channels_last'
    
    def relu6(x):
    	return K.relu(x, max_value=6)
    
    def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
    
    	channel_axis = 1 if IMAGE_ORDERING == 'channels_first' else -1
    	filters = int(filters * alpha)
    	x = ZeroPadding2D(padding=(1, 1), name='conv1_pad', data_format=IMAGE_ORDERING  )(inputs)
    	x = Conv2D(filters, kernel , data_format=IMAGE_ORDERING  ,
    										padding='valid',
    										use_bias=False,
    										strides=strides,
    										name='conv1')(x)
    	x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
    	return Activation(relu6, name='conv1_relu')(x)
    
    def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha,
    													depth_multiplier=1, strides=(1, 1), block_id=1):
    
    	channel_axis = 1 if IMAGE_ORDERING == 'channels_first' else -1
    	pointwise_conv_filters = int(pointwise_conv_filters * alpha)
    
    	x = ZeroPadding2D((1, 1) , data_format=IMAGE_ORDERING , name='conv_pad_%d' % block_id)(inputs)
    	x = DepthwiseConv2D((3, 3) , data_format=IMAGE_ORDERING ,
    														 padding='valid',
    														 depth_multiplier=depth_multiplier,
    														 strides=strides,
    														 use_bias=False,
    														 name='conv_dw_%d' % block_id)(x)
    	x = BatchNormalization(
    			axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)
    	x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x)
    
    	x = Conv2D(pointwise_conv_filters, (1, 1), data_format=IMAGE_ORDERING ,
    										padding='same',
    										use_bias=False,
    										strides=(1, 1),
    										name='conv_pw_%d' % block_id)(x)
    	x = BatchNormalization(axis=channel_axis,
    																name='conv_pw_%d_bn' % block_id)(x)
    	return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)
    
    def get_mobilenet_encoder( input_height=224 ,  input_width=224 , pretrained='imagenet' ):
    
    	alpha=1.0
    	depth_multiplier=1
    	dropout=1e-3
    
    
    	img_input = Input(shape=(input_height,input_width , 3 ))
    
    
    	x = _conv_block(img_input, 32, alpha, strides=(2, 2))
    	x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) 
    	f1 = x
    
    	x = _depthwise_conv_block(x, 128, alpha, depth_multiplier,
    														strides=(2, 2), block_id=2)  
    	x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) 
    	f2 = x
    
    	x = _depthwise_conv_block(x, 256, alpha, depth_multiplier,
    														strides=(2, 2), block_id=4)  
    	x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) 
    	f3 = x
    
    	x = _depthwise_conv_block(x, 512, alpha, depth_multiplier,
    														strides=(2, 2), block_id=6) 
    	x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) 
    	x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) 
    	x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) 
    	x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) 
    	x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) 
    	f4 = x 
    
    	x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier,
    														strides=(2, 2), block_id=12)  
    	x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) 
    	f5 = x 
    
    	return img_input , [f1 , f2 , f3 , f4 , f5 ]

### 2、unet的Decoder解码部分 ###

这一部分对应着上面unet模型中的解码部分。  
其关键就是**把获得的特征重新映射到比较大的图中的每一个像素点，用于每一个像素点的分类。**

from keras.models import *
    from keras.layers import *
    from nets.mobilenet import get_mobilenet_encoder
    
    
    IMAGE_ORDERING = 'channels_last'
    MERGE_AXIS = -1
    
    
    def _unet( n_classes , encoder , l1_skip_conn=True,  input_height=416, input_width=608  ):
    
    	img_input , levels = encoder( input_height=input_height ,  input_width=input_width )
    	[f1 , f2 , f3 , f4 , f5 ] = levels 
    
    	o = f4
    	# 26,26,512
    	o = ( ZeroPadding2D( (1,1) , data_format=IMAGE_ORDERING ))(o)
    	o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
    	o = ( BatchNormalization())(o)
    
    	# 52,52,512
    	o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
    	# 52,52,768
    	o = ( concatenate([ o ,f3],axis=MERGE_AXIS )  )
    	o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
    	# 52,52,256
    	o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o)
    	o = ( BatchNormalization())(o)
    
    	# 104,104,256
    	o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
    	# 104,104,384
    	o = ( concatenate([o,f2],axis=MERGE_AXIS ) )
    	o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
    	# 104,104,128
    	o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING ) )(o)
    	o = ( BatchNormalization())(o)
    	# 208,208,128
    	o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
    	
    	if l1_skip_conn:
    		o = ( concatenate([o,f1],axis=MERGE_AXIS ) )
    
    	o = ( ZeroPadding2D((1,1)  , data_format=IMAGE_ORDERING ))(o)
    	o = ( Conv2D( 64 , (3, 3), padding='valid'  , data_format=IMAGE_ORDERING ))(o)
    	o = ( BatchNormalization())(o)
    
    	o =  Conv2D( n_classes , (3, 3) , padding='same', data_format=IMAGE_ORDERING )( o )
    	
    	# 将结果进行reshape
    	o = Reshape((int(input_height/2)*int(input_width/2), -1))(o)
    	o = Softmax()(o)
    	model = Model(img_input,o)
    
    	return model
    
    
    
    def mobilenet_unet( n_classes ,  input_height=224, input_width=224 , encoder_level=3):
    
    	model =  _unet( n_classes , get_mobilenet_encoder ,  input_height=input_height, input_width=input_width  )
    	model.model_name = "mobilenet_unet"
    	return model

## 代码测试 ##

将上面两个代码分别保存为mobilenet.py和unet.py。按照如下方式存储：  
![在这里插入图片描述][20191108202725368.png_pic_center]  
此时我们运行test.py的代码：

from nets.unet import mobilenet_unet
    model = mobilenet_unet(2,416,416)
    model.summary()

如果没有出错的话就会得到如下的结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 3]  
其模型比segnet稍微大一点。  
到这里就完成了基于Mobile模型的unet的搭建。

# 训练部分 #

## 训练的是什么 ##

虽然把代码贴上来大家就会点运行然后就可以训练自己的模型，但是我还是想要大家知道，语义分割模型训练的是什么。

### 1、训练文件详解 ###

这个要从训练文件讲起。

语义分割模型训练的文件分为两部分。  
第一部分是原图，像这样：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 4]  
第二部分标签，像这样：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 5]  
当你们看到这个标签的时候你们会说，我靠，你给我看的什么辣鸡，全黑的算什么标签，其实并不是这样的，这个标签看起来全黑，**但是实际上在斑马线的部分其RGB三个通道的值都是1。**

**其实给你们换一个图你们就可以更明显的看到了。**  
这是voc数据集中语义分割的训练集中的一幅图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 6]  
这是它的标签。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 7]  
为什么这里的标签看起来就清楚的多呢，**因为在voc中，其一共需要分21类，所以火车的RGB的值可能都大于10了，当然看得见。**

所以，**在训练集中，如果像本文一样分两类，那么背景的RGB就是000，斑马线的RGB就是111，如果分多类，那么还会存在222，333，444这样的。这说明其属于不同的类。**

### 2、LOSS函数的组成 ###

关于loss函数的组成我们需要看两个loss函数的组成部分，第一个是预测结果。

# 此时输出为h_input/2,w_input/2,nclasses
    	o =  Conv2D( n_classes , (3, 3) , padding='same', data_format=IMAGE_ORDERING )( o )
    	# 将结果进行reshape
    	o = Reshape((int(input_height/2)*int(input_width/2), -1))(o)
    	o = Softmax()(o)
    	model = Model(img_input,o)

**其首先利用filter为n\_classes的卷积核进行卷积，此时输出为h\_input/2,w\_input/2,nclasses，对应着每一个hw像素点上的种类。  
之后利用Softmax估计属于每一个种类的概率。**

其最后预测y\_pre其实**就是每一个像素点属于哪一个种类的概率**。

第二个是真实值，真实值是这样处理的。

# 从文件中读取图像
    img = Image.open(r".\dataset2\png" + '/' + name)
    img = img.resize((int(WIDTH/2),int(HEIGHT/2)))
    img = np.array(img)
    seg_labels = np.zeros((int(HEIGHT/2),int(WIDTH/2),NCLASSES))
    for c in range(NCLASSES):
        seg_labels[: , : , c ] = (img[:,:,0] == c ).astype(int)
    seg_labels = np.reshape(seg_labels, (-1,NCLASSES))
    Y_train.append(seg_labels)

其将png图先进行resize，resize后其大小与预测y\_pre的hw相同，然后读取每一个像素点属于什么种类，并存入。

其最后真实y\_true其实**就是每一个像素点确实属于哪个种类**。

最后loss函数的组成就是y\_true和y\_pre的交叉熵。

## 训练代码 ##

大家可以在我的github上下载完整的代码。  
[https://github.com/bubbliiiing/Semantic-Segmentation][https_github.com_bubbliiiing_Semantic-Segmentation]  
数据集的链接为：  
链接：[https://pan.baidu.com/s/1uzwqLaCXcWe06xEXk1ROWw][https_pan.baidu.com_s_1uzwqLaCXcWe06xEXk1ROWw]  
提取码：pp6w

### 1、文件存放方式 ###

如图所示：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 8]  
其中img和img\_out是测试文件。

### 2、训练文件 ###

训练文件如下：

from nets.unet import mobilenet_unet
    from keras.optimizers import Adam
    from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
    from PIL import Image
    import keras
    from keras import backend as K
    import numpy as np
    
    NCLASSES = 2
    HEIGHT = 416
    WIDTH = 416
    
    def generate_arrays_from_file(lines,batch_size):
        # 获取总长度
        n = len(lines)
        i = 0
        while 1:
            X_train = []
            Y_train = []
            # 获取一个batch_size大小的数据
            for _ in range(batch_size):
                if i==0:
                    np.random.shuffle(lines)
                name = lines[i].split(';')[0]
                # 从文件中读取图像
                img = Image.open(r".\dataset2\jpg" + '/' + name)
                img = img.resize((WIDTH,HEIGHT))
                img = np.array(img)
                img = img/255
                X_train.append(img)
    
                name = (lines[i].split(';')[1]).replace("\n", "")
                # 从文件中读取图像
                img = Image.open(r".\dataset2\png" + '/' + name)
                img = img.resize((int(WIDTH/2),int(HEIGHT/2)))
                img = np.array(img)
                seg_labels = np.zeros((int(HEIGHT/2),int(WIDTH/2),NCLASSES))
                for c in range(NCLASSES):
                    seg_labels[: , : , c ] = (img[:,:,0] == c ).astype(int)
                seg_labels = np.reshape(seg_labels, (-1,NCLASSES))
                Y_train.append(seg_labels)
    
                # 读完一个周期后重新开始
                i = (i+1) % n
            yield (np.array(X_train),np.array(Y_train))
    
    def loss(y_true, y_pred):
        crossloss = K.binary_crossentropy(y_true,y_pred)
        loss = 4 * K.sum(crossloss)/HEIGHT/WIDTH
        return loss
    
    if __name__ == "__main__":
        log_dir = "logs/"
        # 获取model
        model = mobilenet_unet(n_classes=NCLASSES,input_height=HEIGHT, input_width=WIDTH)
        # model.summary()
        BASE_WEIGHT_PATH = ('https://github.com/fchollet/deep-learning-models/'
    										'releases/download/v0.6/')
        model_name = 'mobilenet_%s_%d_tf_no_top.h5' % ( '1_0' , 224 )
       
        weight_path = BASE_WEIGHT_PATH + model_name
        weights_path = keras.utils.get_file(model_name, weight_path )
        print(weight_path)
        model.load_weights(weights_path,by_name=True,skip_mismatch=True)
    
        # model.summary()
        # 打开数据集的txt
        with open(r".\dataset2\train.txt","r") as f:
            lines = f.readlines()
    
        # 打乱行，这个txt主要用于帮助读取数据来训练
        # 打乱的数据更有利于训练
        np.random.seed(10101)
        np.random.shuffle(lines)
        np.random.seed(None)
    
        # 90%用于训练，10%用于估计。
        num_val = int(len(lines)*0.1)
        num_train = len(lines) - num_val
    
        # 保存的方式，1世代保存一次
        checkpoint_period = ModelCheckpoint(
                                        log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
                                        monitor='val_loss', 
                                        save_weights_only=True, 
                                        save_best_only=True, 
                                        period=1
                                    )
        # 学习率下降的方式，val_loss三次不下降就下降学习率继续训练
        reduce_lr = ReduceLROnPlateau(
                                monitor='val_loss', 
                                factor=0.5, 
                                patience=3, 
                                verbose=1
                            )
        # 是否需要早停，当val_loss一直不下降的时候意味着模型基本训练完毕，可以停止
        early_stopping = EarlyStopping(
                                monitor='val_loss', 
                                min_delta=0, 
                                patience=10, 
                                verbose=1
                            )
    
        # 交叉熵
        model.compile(loss = loss,
                optimizer = Adam(lr=1e-3),
                metrics = ['accuracy'])
        batch_size = 2
        print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
        
        # 开始训练
        model.fit_generator(generate_arrays_from_file(lines[:num_train], batch_size),
                steps_per_epoch=max(1, num_train//batch_size),
                validation_data=generate_arrays_from_file(lines[num_train:], batch_size),
                validation_steps=max(1, num_val//batch_size),
                epochs=50,
                initial_epoch=0,
                callbacks=[checkpoint_period, reduce_lr])
    
        model.save_weights(log_dir+'last1.h5')

### 3、预测文件 ###

预测文件如下：

from nets.unet import mobilenet_unet
    from PIL import Image
    import numpy as np
    import random
    import copy
    import os
    
    random.seed(0)
    class_colors = [[0,0,0],[0,255,0]]
    NCLASSES = 2
    HEIGHT = 416
    WIDTH = 416
    
    
    model = mobilenet_unet(n_classes=NCLASSES,input_height=HEIGHT, input_width=WIDTH)
    model.load_weights("logs/ep015-loss0.070-val_loss0.076.h5")
    
    imgs = os.listdir("./img")
    
    for jpg in imgs:
    
        img = Image.open("./img/"+jpg)
        old_img = copy.deepcopy(img)
        orininal_h = np.array(img).shape[0]
        orininal_w = np.array(img).shape[1]
    
        img = img.resize((WIDTH,HEIGHT))
        img = np.array(img)
        img = img/255
        img = img.reshape(-1,HEIGHT,WIDTH,3)
        pr = model.predict(img)[0]
    
        pr = pr.reshape((int(HEIGHT/2), int(WIDTH/2),NCLASSES)).argmax(axis=-1)
    
        seg_img = np.zeros((int(HEIGHT/2), int(WIDTH/2),3))
        colors = class_colors
    
        for c in range(NCLASSES):
            seg_img[:,:,0] += ( (pr[:,: ] == c )*( colors[c][0] )).astype('uint8')
            seg_img[:,:,1] += ((pr[:,: ] == c )*( colors[c][1] )).astype('uint8')
            seg_img[:,:,2] += ((pr[:,: ] == c )*( colors[c][2] )).astype('uint8')
    
        seg_img = Image.fromarray(np.uint8(seg_img)).resize((orininal_w,orininal_h))
    
        image = Image.blend(old_img,seg_img,0.3)
        image.save("./img_out/"+jpg)

## 训练结果 ##

原图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 9]  
处理后：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 10]  
按照道理到说，unet模型相比于segnet模型更加复杂，提取的特征层更多，应该效果会更好，不过由于我用的图片比较少，可能过拟合了，几个测试图片的效果都一般，不过在更复杂的图片上，应该是unet效果更好才对……

[2_segnet]: https://blog.csdn.net/weixin_44791964/article/details/102980249
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20190723144346254.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 1]: https://img-blog.csdnimg.cn/20191109101342389.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 2]: https://img-blog.csdnimg.cn/20191109102824157.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[23_MobileNet]: https://blog.csdn.net/weixin_44791964/article/details/102819915
[20191108202725368.png_pic_center]: https://img-blog.csdnimg.cn/20191108202725368.png#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 3]: https://img-blog.csdnimg.cn/20191109112201280.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 4]: https://img-blog.csdnimg.cn/20191108211319488.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 5]: https://img-blog.csdnimg.cn/2019110821140677.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 6]: https://img-blog.csdnimg.cn/20191108211712526.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 7]: https://img-blog.csdnimg.cn/20191108211726987.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[https_github.com_bubbliiiing_Semantic-Segmentation]: https://github.com/bubbliiiing/Semantic-Segmentation
[https_pan.baidu.com_s_1uzwqLaCXcWe06xEXk1ROWw]: https://pan.baidu.com/s/1uzwqLaCXcWe06xEXk1ROWw
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 8]: https://img-blog.csdnimg.cn/20191109110927374.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 9]: https://img-blog.csdnimg.cn/20191108215702616.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
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