Convolution parameters in the Alex/overfeat/vgg

Source: Internet
Author: User
Tags scale image

The convolution parameters of some classical CNN structures have been statistically studied.

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).

Relationship between Output_size and Input_size/kernel_size/padding/stride

OuT _sIZe=I N_sIZe?K eR Nel _sIZe+2xPad _sIZes t r i d e +1

Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.

Convolution parameters in the Alex/overfeat/vgg

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.