Table 2.

Details of the pre-train model at each ConvNet’s layer (unit: pixel).

Layer Type			Kernel Attribute	Num of Filters
Image Input Layer
Pre-train model—VGG16 [20]	Conv1	Convolutional Layer	3 × 3, stride = 1, padding = same	64
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	64
		ReLU Layer
		Max Pooling		2 x 2
	Conv2	Convolutional Layer	3 × 3, stride = 1, padding = same	128
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	128
		ReLU Layer
		Max Pooling		2 × 2
	Conv3	Convolutional Layer	3 × 3, stride = 1, padding = same	256
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	256
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	256
		ReLU Layer
		Max Pooling		2 × 2
	Conv4	Convolutional Layer	3 × 3, stride = 1, padding = same	512
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	512
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	512
		ReLU Layer
		Max Pooling		2 × 2
	Conv5	Convolutional Layer	3 × 3, stride = 1, padding = same	512
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	512
		ReLU Layer
		Convolutional Layer	3 × 3, stride = 1, padding = same	512
		ReLU Layer
		Max Pooling		2 × 2