FCN-8 - Encoder

# FCN-8 - Encoder
Let’s focus on a concrete implementation of a fully convolutional network. We’ll discuss the FCN-8 architecture developed at Berkeley. In fact, many FCN models are derived from this FCN-8 implementation. The encoder for FCN-8 is the VGG16 model pretrained on ImageNet for classification. The fully-connected layers are replaced by 1-by-1 convolutions. Here’s an example of going from a fully-connected layer to a 1-by-1 convolution in TensorFlow:

num_classes = 2
output = tf.layers.dense(input, num_classes)

To:

num_classes = 2
output = tf.layers.conv2d(input, num_classes, 1, strides=(1,1))

The third argument, 1 , is the kernel size, meaning this is a 1 by 1 convolution.
Thus far, we’ve downsampled the input image and extracted features using the VGG16 encoder. We’ve also replaced the linear layers with 1 by 1 convolutional layers, preserving spatial information.

But this is just the encoder portion of the network. Next comes the decoder.

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