我尝试在Berkeley DeepDrive数据集上训练在Keras中实现的SegNet-Basic。但是在训练之后，验证准确度会稳定在50-60％左右，而训练准确度则会上升到90％以上。
我尝试使用不同的优化器和图片尺寸。数据集的遮罩以灰度图像的形式出现，我根据provided labels将它们分成一个numpy数组，每张图片[img_height，img_width，num_classes]。
我的SegNet_Basic实现如下所示：_encode和_decode函数是Unpool的实现，其中paper声明了索引。
def SegNet_Basic(n_labels,input_shape= (480,352,3)):
kernel = 3
filter_size = 64
inputs = Input(shape=input_shape)
c1 = Conv2D(filter_size, (3,3),activation='relu',padding='same')(inputs)
c1 = BatchNormalization()(c1)
pool_1, mask_1 = _encode(c1)
c2 = Dropout(0.2)(pool_1)
c2 = Conv2D(128, (3,3),activation='relu', padding='same')(c2)
c2 = BatchNormalization()(c2)
pool_2, mask_2 = _encode(c2)
c3 = Conv2D(256, (3,3),activation='relu', padding='same')(pool_2)
c3 = BatchNormalization()(c3)
pool_3, mask_3 = _encode(c3)
c4 = Dropout(0.2)(pool_3)
c4 = Conv2D(512, (3,3),activation='relu', padding='same')(c4)
c4 = BatchNormalization()(c4)
c5 = Conv2D(512, (3,3), activation='relu',padding='same')(c4)
c5 = BatchNormalization()(c5)
c6 = Conv2D(256, (3, 3), activation='relu', padding='same')(c5)
unpool_1 = _decode(c6,mask_3)
c6 = Dropout(0.2)(unpool_1)
c6 = Conv2D(256, (3,3),activation='relu', padding='same')(c6)
c6 = BatchNormalization()(c6)
c7 = Conv2D(128, (3, 3), activation='relu', padding='same')(c6)
unpool_2 = _decode(c7,mask_2)
c7 = Dropout(0.2)(unpool_2)
c7 = Conv2D(128,(3,3), activation='relu',padding='same')(c7)
c7 = BatchNormalization()(c7)
c8 = Conv2D(64, (3, 3), activation='relu', padding='same')(c7)
unpool_3 = _decode(c8,mask_1)
c8 = Conv2D(filter_size,(3,3),activation='relu', padding='same')(unpool_3)
c8 = BatchNormalization()(c8)
output = Conv2D(n_labels, (1, 1), activation='softmax')(c8)
model = Model(inputs=[inputs], output=[output], name='SegNet_Basic')
return model
我使用的图片大小为（480,352），因为我的笔记本电脑无法在gpu上处理它们，因此我无法使用更大的尺寸。我还看到，如我添加的第一张图片所示，像天空和树木这样的类得到了很好的认可，但是其他类的准确性却非常糟糕。
如果需要，我还可以提供代码的其他部分！
谢谢您的帮助！
