我的问题就是这样:noise in image segmentation result
但我不知道怎么解决我的问题
我的模型定义为:
conv1_1 = self.conv2d(img, 64, layer_name = "conv1_1")
conv1_2 = self.conv2d(conv1_1, 64, layer_name = "conv1_2")
pool1 = self.max_pool(conv1_2, "pool1")
conv2_1 = self.conv2d(pool1, 128, layer_name = "conv2_1")
conv2_2 = self.conv2d(conv2_1, 128, layer_name = "conv2_2")
pool2 = self.max_pool(conv2_2, "pool2")
conv3_1 = self.conv2d(pool2, 256, layer_name = "conv3_1")
conv3_2 = self.conv2d(conv3_1, 256, layer_name = "conv3_2")
conv3_3 = self.conv2d(conv3_2, 256, layer_name = "conv3_3")
pool3 = self.max_pool(conv3_3, "pool3")
conv4_1 = self.conv2d(pool3, 512, layer_name = "conv4_1")
conv4_2 = self.conv2d(conv4_1, 512, layer_name = "conv4_2")
conv4_3 = self.conv2d(conv4_2, 512, layer_name = "conv4_3")
pool4 = self.max_pool(conv4_3, "pool4")
print("pool4: ", pool4.shape)
conv5_1 = self.conv2d(pool4, 512, layer_name = "conv5_1")
conv5_2 = self.conv2d(conv5_1, 512, layer_name = "conv5_2")
conv5_3 = self.conv2d(conv5_2, 512, layer_name = "conv5_3")
pool5 = self.max_pool(conv5_3, "pool5")
conv6 = self.conv2d(pool5, 4096, 7, layer_name = "conv6")
conv7 = self.conv2d(conv6, 4096, 1, layer_name = "conv7")
print("conv7: ", conv7.shape)
pool3_1x1 = self.conv2d(pool3, 2, k_size = 1, layer_name = "pool3_1x1")
pool4_1x1 = self.conv2d(pool4, 2, k_size = 1, layer_name = "pool4_1x1")
conv7_1x1 = self.conv2d(conv7, 2, k_size = 1, layer_name = "conv7_1x1")
print("conv7_1x1: ", conv7_1x1.shape)
deconv7 = self.upsample(conv7_1x1, 2, k = 4, s = 2, layer_name = "deconv7")
print("deconv7: ", deconv7.shape)
fuse1 = tf.add(deconv7, pool4_1x1)
deconv_fuse1 = self.upsample(fuse1, 2, k = 4, s = 2, layer_name = "deconv_fuse1")
print("deconv_fuse1: ", deconv_fuse1.shape)
fuse2 = tf.add(deconv_fuse1, pool3_1x1)
out = self.upsample(fuse2, 2, k = 16, s = 8, layer_name = "out")
print("out: ", out.shape)
score_map = tf.nn.sigmoid(out)
我真正想要的是:
白色为S形值>;=0.5 黑色是sigmoid值<;0.5英寸
有人能告诉我怎么修改代码吗
我的输入图像形状是128x128x3
目前没有回答
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