Table 1.

The detailed architecture of CAC-SPP.

Layers		Shape
		kernel	padding	atrous	stride	output	number of parameters
Input data		-	-	-	-	417×417×1
conv1		[7 × 7]	3	1	2	209×209×64	3,200
max-pool 1		[3 × 3]	1	1	2	105×105×64
conv2		$\left[\begin{array}{ccc}1& \times & 1\\ 3& \times & 3\\ 1& \times & 1\end{array}\right]\times 3$	$\left[\begin{array}{c}0\\ 1\\ 0\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	105×105×64 105×105×64 105×105×256	173,184
conv3		$\left[\begin{array}{ccc}1& \times & 1\\ 3& \times & 3\\ 1& \times & 1\end{array}\right]\times 4$	$\left[\begin{array}{c}2\\ 1\\ 0\end{array}\right]$	$\left[\begin{array}{c}2\\ 1\\ 1\end{array}\right]$	$\left[\begin{array}{c}2\\ 1\\ 1\end{array}\right]$	53×53×128 53×53×128 53×53×512	986,112
conv4		$\left[\begin{array}{ccc}1& \times & 1\\ 3& \times & 3\\ 1& \times & 1\end{array}\right]\times 23$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	53×53×256 53×53×256 53×53×1024	22,645,248
CAC	conv5-b1	$\left[\begin{array}{ccc}1& \times & 1\\ 3& \times & 3\\ 1& \times & 1\end{array}\right]\times 3$	$\left[\begin{array}{c}2\\ 2\\ 2\end{array}\right]$	$\left[\begin{array}{c}2\\ 2\\ 2\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	53×53×512 53×53×512 53×53×2048	11,805,696
	conv5-b2	$\left[\begin{array}{ccc}1& \times & 1\\ 3& \times & 3\\ 1& \times & 1\end{array}\right]\times 3$	$\left[\begin{array}{c}4\\ 4\\ 4\end{array}\right]$	$\left[\begin{array}{c}4\\ 4\\ 4\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	53×53×512 53×53×512 53×53×2048	13,378,560
	conv5-b3	$\left[\begin{array}{ccc}1& \times & 1\\ 3& \times & 3\\ 1& \times & 1\end{array}\right]\times 3$	$\left[\begin{array}{c}8\\ 8\\ 8\end{array}\right]$	$\left[\begin{array}{c}8\\ 8\\ 8\end{array}\right]$	$\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]$	53×53×512 53×53×512 53×53×2048	13,378,560
SPP	spp-conv1	[1 × 1]	0	1	1	53×53×1024	2,098,176
	spp-conv2	[3 × 3]	4	4	1	53×53×1024	18,875,392
	spp-conv3	[3 × 3]	8	8	1	53×53×1024	18,875,392
	spp-conv4	[3 × 3]	12	12	1	53×53×1024	18,875,392
concat		spp-conv1 + spp-conv2 + spp-conv3 + spp-conv4
fc		[1 × 1]	0	1	1	53×53×2	4,098
interpolation		factor = 8				417×417×2
Output data						417×417×1