RGB图像的多类语义分割困难

Question

有人能帮我解决这个多类语义分割的问题吗？我已经修改了一个代码来接受RGB图像和RGB标签作为蒙版。我正在使用下面的模型

​

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
img (InputLayer)                (None, 128, 128, 3)  0                                            
__________________________________________________________________________________________________
conv2d_170 (Conv2D)             (None, 128, 128, 16) 448         img[0][0]                        
__________________________________________________________________________________________________
batch_normalization_163 (BatchN (None, 128, 128, 16) 64          conv2d_170[0][0]                 
__________________________________________________________________________________________________
activation_163 (Activation)     (None, 128, 128, 16) 0           batch_normalization_163[0][0]    
__________________________________________________________________________________________________
conv2d_171 (Conv2D)             (None, 128, 128, 16) 2320        activation_163[0][0]             
__________________________________________________________________________________________________
batch_normalization_164 (BatchN (None, 128, 128, 16) 64          conv2d_171[0][0]                 
__________________________________________________________________________________________________
activation_164 (Activation)     (None, 128, 128, 16) 0           batch_normalization_164[0][0]    
__________________________________________________________________________________________________
max_pooling2d_37 (MaxPooling2D) (None, 64, 64, 16)   0           activation_164[0][0]             
__________________________________________________________________________________________________
dropout_73 (Dropout)            (None, 64, 64, 16)   0           max_pooling2d_37[0][0]           
__________________________________________________________________________________________________
conv2d_172 (Conv2D)             (None, 64, 64, 32)   4640        dropout_73[0][0]                 
__________________________________________________________________________________________________
batch_normalization_165 (BatchN (None, 64, 64, 32)   128         conv2d_172[0][0]                 
__________________________________________________________________________________________________
activation_165 (Activation)     (None, 64, 64, 32)   0           batch_normalization_165[0][0]    
__________________________________________________________________________________________________
conv2d_173 (Conv2D)             (None, 64, 64, 32)   9248        activation_165[0][0]             
__________________________________________________________________________________________________
batch_normalization_166 (BatchN (None, 64, 64, 32)   128         conv2d_173[0][0]                 
__________________________________________________________________________________________________
activation_166 (Activation)     (None, 64, 64, 32)   0           batch_normalization_166[0][0]    
__________________________________________________________________________________________________
max_pooling2d_38 (MaxPooling2D) (None, 32, 32, 32)   0           activation_166[0][0]             
__________________________________________________________________________________________________
dropout_74 (Dropout)            (None, 32, 32, 32)   0           max_pooling2d_38[0][0]           
__________________________________________________________________________________________________
conv2d_174 (Conv2D)             (None, 32, 32, 64)   18496       dropout_74[0][0]                 
__________________________________________________________________________________________________
batch_normalization_167 (BatchN (None, 32, 32, 64)   256         conv2d_174[0][0]                 
__________________________________________________________________________________________________
activation_167 (Activation)     (None, 32, 32, 64)   0           batch_normalization_167[0][0]    
__________________________________________________________________________________________________
conv2d_175 (Conv2D)             (None, 32, 32, 64)   36928       activation_167[0][0]             
__________________________________________________________________________________________________
batch_normalization_168 (BatchN (None, 32, 32, 64)   256         conv2d_175[0][0]                 
__________________________________________________________________________________________________
activation_168 (Activation)     (None, 32, 32, 64)   0           batch_normalization_168[0][0]    
__________________________________________________________________________________________________
max_pooling2d_39 (MaxPooling2D) (None, 16, 16, 64)   0           activation_168[0][0]             
__________________________________________________________________________________________________
dropout_75 (Dropout)            (None, 16, 16, 64)   0           max_pooling2d_39[0][0]           
__________________________________________________________________________________________________
conv2d_176 (Conv2D)             (None, 16, 16, 128)  73856       dropout_75[0][0]                 
__________________________________________________________________________________________________
batch_normalization_169 (BatchN (None, 16, 16, 128)  512         conv2d_176[0][0]                 
__________________________________________________________________________________________________
activation_169 (Activation)     (None, 16, 16, 128)  0           batch_normalization_169[0][0]    
__________________________________________________________________________________________________
conv2d_177 (Conv2D)             (None, 16, 16, 128)  147584      activation_169[0][0]             
__________________________________________________________________________________________________
batch_normalization_170 (BatchN (None, 16, 16, 128)  512         conv2d_177[0][0]                 
__________________________________________________________________________________________________
activation_170 (Activation)     (None, 16, 16, 128)  0           batch_normalization_170[0][0]    
__________________________________________________________________________________________________
max_pooling2d_40 (MaxPooling2D) (None, 8, 8, 128)    0           activation_170[0][0]             
__________________________________________________________________________________________________
dropout_76 (Dropout)            (None, 8, 8, 128)    0           max_pooling2d_40[0][0]           
__________________________________________________________________________________________________
conv2d_178 (Conv2D)             (None, 8, 8, 256)    295168      dropout_76[0][0]                 
__________________________________________________________________________________________________
batch_normalization_171 (BatchN (None, 8, 8, 256)    1024        conv2d_178[0][0]                 
__________________________________________________________________________________________________
activation_171 (Activation)     (None, 8, 8, 256)    0           batch_normalization_171[0][0]    
__________________________________________________________________________________________________
conv2d_179 (Conv2D)             (None, 8, 8, 256)    590080      activation_171[0][0]             
__________________________________________________________________________________________________
batch_normalization_172 (BatchN (None, 8, 8, 256)    1024        conv2d_179[0][0]                 
__________________________________________________________________________________________________
activation_172 (Activation)     (None, 8, 8, 256)    0           batch_normalization_172[0][0]    
__________________________________________________________________________________________________
conv2d_transpose_37 (Conv2DTran (None, 16, 16, 128)  295040      activation_172[0][0]             
__________________________________________________________________________________________________
concatenate_37 (Concatenate)    (None, 16, 16, 256)  0           conv2d_transpose_37[0][0]        
                                                                 activation_170[0][0]             
__________________________________________________________________________________________________
dropout_77 (Dropout)            (None, 16, 16, 256)  0           concatenate_37[0][0]             
__________________________________________________________________________________________________
conv2d_180 (Conv2D)             (None, 16, 16, 128)  295040      dropout_77[0][0]                 
__________________________________________________________________________________________________
batch_normalization_173 (BatchN (None, 16, 16, 128)  512         conv2d_180[0][0]                 
__________________________________________________________________________________________________
activation_173 (Activation)     (None, 16, 16, 128)  0           batch_normalization_173[0][0]    
__________________________________________________________________________________________________
conv2d_181 (Conv2D)             (None, 16, 16, 128)  147584      activation_173[0][0]             
__________________________________________________________________________________________________
batch_normalization_174 (BatchN (None, 16, 16, 128)  512         conv2d_181[0][0]                 
__________________________________________________________________________________________________
activation_174 (Activation)     (None, 16, 16, 128)  0           batch_normalization_174[0][0]    
__________________________________________________________________________________________________
conv2d_transpose_38 (Conv2DTran (None, 32, 32, 64)   73792       activation_174[0][0]             
__________________________________________________________________________________________________
concatenate_38 (Concatenate)    (None, 32, 32, 128)  0           conv2d_transpose_38[0][0]        
                                                                 activation_168[0][0]             
__________________________________________________________________________________________________
dropout_78 (Dropout)            (None, 32, 32, 128)  0           concatenate_38[0][0]             
__________________________________________________________________________________________________
conv2d_182 (Conv2D)             (None, 32, 32, 64)   73792       dropout_78[0][0]                 
__________________________________________________________________________________________________
batch_normalization_175 (BatchN (None, 32, 32, 64)   256         conv2d_182[0][0]                 
__________________________________________________________________________________________________
activation_175 (Activation)     (None, 32, 32, 64)   0           batch_normalization_175[0][0]    
__________________________________________________________________________________________________
conv2d_183 (Conv2D)             (None, 32, 32, 64)   36928       activation_175[0][0]             
__________________________________________________________________________________________________
batch_normalization_176 (BatchN (None, 32, 32, 64)   256         conv2d_183[0][0]                 
__________________________________________________________________________________________________
activation_176 (Activation)     (None, 32, 32, 64)   0           batch_normalization_176[0][0]    
__________________________________________________________________________________________________
conv2d_transpose_39 (Conv2DTran (None, 64, 64, 32)   18464       activation_176[0][0]             
__________________________________________________________________________________________________
concatenate_39 (Concatenate)    (None, 64, 64, 64)   0           conv2d_transpose_39[0][0]        
                                                                 activation_166[0][0]             
__________________________________________________________________________________________________
dropout_79 (Dropout)            (None, 64, 64, 64)   0           concatenate_39[0][0]             
__________________________________________________________________________________________________
conv2d_184 (Conv2D)             (None, 64, 64, 32)   18464       dropout_79[0][0]                 
__________________________________________________________________________________________________
batch_normalization_177 (BatchN (None, 64, 64, 32)   128         conv2d_184[0][0]                 
__________________________________________________________________________________________________
activation_177 (Activation)     (None, 64, 64, 32)   0           batch_normalization_177[0][0]    
__________________________________________________________________________________________________
conv2d_185 (Conv2D)             (None, 64, 64, 32)   9248        activation_177[0][0]             
__________________________________________________________________________________________________
batch_normalization_178 (BatchN (None, 64, 64, 32)   128         conv2d_185[0][0]                 
__________________________________________________________________________________________________
activation_178 (Activation)     (None, 64, 64, 32)   0           batch_normalization_178[0][0]    
__________________________________________________________________________________________________
conv2d_transpose_40 (Conv2DTran (None, 128, 128, 16) 4624        activation_178[0][0]             
__________________________________________________________________________________________________
concatenate_40 (Concatenate)    (None, 128, 128, 32) 0           conv2d_transpose_40[0][0]        
                                                                 activation_164[0][0]             
__________________________________________________________________________________________________
dropout_80 (Dropout)            (None, 128, 128, 32) 0           concatenate_40[0][0]             
__________________________________________________________________________________________________
conv2d_186 (Conv2D)             (None, 128, 128, 16) 4624        dropout_80[0][0]                 
__________________________________________________________________________________________________
batch_normalization_179 (BatchN (None, 128, 128, 16) 64          conv2d_186[0][0]                 
__________________________________________________________________________________________________
activation_179 (Activation)     (None, 128, 128, 16) 0           batch_normalization_179[0][0]    
__________________________________________________________________________________________________
conv2d_187 (Conv2D)             (None, 128, 128, 16) 2320        activation_179[0][0]             
__________________________________________________________________________________________________
batch_normalization_180 (BatchN (None, 128, 128, 16) 64          conv2d_187[0][0]                 
__________________________________________________________________________________________________
activation_180 (Activation)     (None, 128, 128, 16) 0           batch_normalization_180[0][0]    
__________________________________________________________________________________________________
conv2d_188 (Conv2D)             (None, 128, 128, 1)  17          activation_180[0][0]             
==================================================================================================
Total params: 2,164,593
Trainable params: 2,161,649
Non-trainable params: 2,944
__________________________________________________________________________________________________

​

如你所见，输入有3个通道。最后一层应该有11个通道中的1个通道吗？我使用的数据集有11个类，它们在图像中由不同的RGB值组合表示。

谢谢。

Answer 1

最后一层应该是11个通道，对应于每个像素位置的11个类。这就像对每个像素位置进行多类分类一样。