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我从残差模型中得到的输出是一个带有小方块的图像（一个非常低分辨率的图像），但它应该给我一个深度图。图像中的对象丢失了，只有那些小方块是可见的。我不会即兴创作？

定义迷你模型（输入形状）：

# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)

# Zero_Padding
X = ZeroPadding2D((3,3))(X_input)
#stage_1
X = Conv2D(64,(7,7),strides = (2,2),name = 'conv1')(X)
X = BatchNormalization(axis = 3,name = 'bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3,3),strides = (2,2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)
X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')

#stage3
X = convolutional_block(X,f = 3 , filters = [128,128,512],stage = 3,block = 'a', s = 2)
X = identity_block(X,3,[128,128,512],stage = 3,block='b')
X = identity_block(X,3,[128,128,512],stage = 3 , block = 'c')
X = identity_block(X,3,[128,128,512],stage = 3 , block = 'd')

#stage 4
X = convolutional_block(X,f = 3 , filters = [256,256,1024],stage = 4,block = 'a', s = 2)
X = identity_block(X,3,[256,256,1024],stage = 4,block='b')
X = identity_block(X,3,[256,256,1024],stage = 4,block='c')
X = identity_block(X,3,[256,256,1024],stage = 4,block='d')
X = identity_block(X,3,[256,256,1024],stage = 4,block='e')
X = identity_block(X,3,[256,256,1024],stage = 4,block='f')
X = identity_block(X,3,[256,256,1024],stage = 4,block='g')
X = identity_block(X,3,[256,256,1024],stage = 4,block='h')
X = identity_block(X,3,[256,256,1024],stage = 4,block='i')
X = identity_block(X,3,[256,256,1024],stage = 4,block='j')
X = identity_block(X,3,[256,256,1024],stage = 4,block='k')
X = identity_block(X,3,[256,256,1024],stage = 4,block='l')

#stage 5
X = convolutional_block(X,f = 3 , filters = [512,512,2048],stage = 5,block = 'a', s = 2)
X = identity_block(X,3,[512,512,2048],stage = 5,block='b')
X = identity_block(X,3,[512,512,2048],stage = 5,block='c')

# AVGPOOL

    X = Conv2D(3,kernel_size=(3,3), padding = 'same',use_bias = False)(X)
    X = UpSampling2D(size=2)(X)
    X = UpSampling2D(size=2)(X)
    X = UpSampling2D(size=2)(X)
    X = UpSampling2D(size=2)(X)
X = UpSampling2D(size=2)(X)

# Create model
model = Model(inputs = X_input, outputs = X)
return(model)

我的残留模型！！输入图像形状 = (480,640,3)

在此处输入图像描述

实际效果：图像由小方块组成，灰度等级不同。预期结果：图像应该是与输入（480,640,3）大小相同的深度图

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1 回答 1

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您依次有五个上采样层。这正是预期的结果。32 像素的大方块。(2^5 = 32)

您可能应该阅读有关 U-net 的信息，在上采样和从 resnet 到上采样结果的连接之间创建更多卷积。

于 2018-12-26T15:03:09.610 回答