Table 1.

Detailed layer information for our deep CNN configuration

Layer	Type	Filter size	# of Feature maps	Spatial size
0	Input		1	$1000\times 1000$
1	Resize		1	$224\times 224$
2	Conv1 + ReLU	$5\times 5$	32	$224\times 224$
3	Pool1	$2\times 2$	32	$112\times 112$
4	Conv2 + ReLU	$5\times 5$	32	$112\times 112$
5	Pool2	$2\times 2$	32	$56\times 56$
6	Conv3 + ReLU	$5\times 5$	48	$56\times 56$
7	Pool3	$2\times 2$	48	$28\times 28$
8	Conv4 + ReLU	$5\times 5$	48	$28\times 28$
9	Pool4	$2\times 2$	48	$14\times 14$
10	Conv5 + ReLU	$5\times 5$	64	$14\times 14$
11	Conv6 + ReLU	$5\times 5$	64	$14\times 14$
12	Pool6	$2\times 2$	64	$7\times 7$
13	Conv7 + ReLU	$5\times 5$	128	$7\times 7$
14	FC1 + ReLU + Dropout		1024	$1\times 1$
15	FC2 + Softmax		2	$1\times 1$
16	Output