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

LayerInputKernelOutputStridePad
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

LayerInputKernelOutputStridePad
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

LayerInputKernelOutputStridePad
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_size)/Stride+1
posted @ 2018-01-13 17:36  木易修  阅读(289)  评论(0)  编辑  收藏  举报