Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures

Table 2

The detailed feature size of each layer in the Inception_v3 architecture.

Layers	Input size	Pad	Kernel size	Output size
Conv1-s2	( = 5~12)	0		( = 18~21)
Conv2	( = 18~21)	0		( = 16~19)
Conv3	( = 16~19)	1		( = 16~19)
MaxPool-s2	( = 16~19)	0		or
Conv4_1	or	0		or
Conv4_2	or	0		or
MaxPool-s2	or	0
Inc_a1~Inc_a3		1
Inc_a-s2
Conv		0
Pool		0
Inc_b1~Inc_b4		1
Inc_b-s2
Conv		0
Pool		0
Inc_c1~Inc_c2		1
GAP
Fc_1000
Softmax