Alex / OverFeat / VGG 中的卷积参数

研究需要，统计了一些经典CNN结构的卷积层参数。

Alexnet

Layer	Input	Kernel	Output	Stride	Pad
1	256 * 3 * 227 * 227	48 * 3 * 11 * 11	256 * 48 * 55 * 55	4	0
2	256 * 48 * 27 * 27	128 * 48 * 5 * 5	256 * 128 * 27 * 27	1	2
3	256 * 128 * 13 * 13	192 * 128 * 3 * 3	256 * 192 * 13 * 13	1	1
4	256 * 192 * 13 * 13	192 * 192 * 3 * 3	256 * 192 * 13 * 13	1	1
5	256 * 192 * 13 * 13	192 * 192 * 3 * 3	256 * 192 * 13 * 13	1	1

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.

Over Feat

Layer	Input	Kernel	Output	Stride	Pad
1	128 * 3 * 221 * 221	96 * 3 * 11 * 11	128 * 96 * 106 * 106	2	0
2	128 * 96 * 58 * 58	256 * 96 * 5 * 5	128 * 96 * 54 * 54	1	0
3	128 * 96 * 27 *27	512 * 96 * 3 * 3	128 * 512 * 27 * 27	1	1
4	128 * 512 * 27 * 27	1024 * 512 * 3 * 3	128 * 1024 * 27 * 27	1	1
5	128 * 1024 * 27 * 27	1024 * 1024 * 3 * 3	128 * 1024 * 27 * 27	1	1

Sermanet, Pierre, et al. “Overfeat: Integrated recognition, localization and detection using convolutional networks.” arXiv preprint arXiv:1312.6229 (2013).

VGG

Layer	Input	Kernel	Output	Stride	Pad
1	256 * 3 * 224 * 224	64 * 3 * 3 * 3	256 * 64 * 222 * 222	1	0
2	256 * 64 * 222 * 222	64 * 64 * 3 * 3	256 * 64 * 220 * 220	1	0
3	256 * 64 * 110 * 110	128 * 64 * 3 * 3	256 * 128 * 108 * 108	1	0
4	256 * 128 * 108 * 108	128 * 128 * 3 * 3	256 * 128 * 106 * 106	1	0
5	256 * 128 * 58 * 58	256 * 128 * 3 * 3	256 * 256 * 56 * 56	1	0
6	256 * 256 * 56 * 56	256 * 256 * 3 * 3	256 * 256 * 54 * 54	1	0
7	256 * 256 * 54 * 54	256 * 256 * 3 * 3	256 * 256 * 52 * 52	1	0
8	256 * 256 * 52 * 52	256 * 256 * 3 * 3	256 * 256 * 52 * 52	1	1
9	256 * 256 * 26 * 26	512 * 256 * 3 * 3	256 * 512 * 24 * 24	1	0
10	256 * 512 * 24 * 24	512 * 512 * 3 * 3	256 * 512 * 22 * 22	1	0
11	256 * 512 * 22 * 22	512 * 512 * 3 * 3	256 * 512 * 20 * 20	1	0
12	256 * 512 * 20 * 20	512 * 512 * 3 * 3	256 * 512 * 18 * 18	1	0

Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).

Output_size 与 Input_size/ Kernel_size / Padding / Stride 关系

Out_size=In_size−Kernel_size+2×Pad_sizeStride+1