luuuyi/igcv_v1.pytorch

re-implement for paper: Interleaved Group Convolutions for Deep Neural Networks. (IGCV V1)

Interleaved Group Convolutions for Deep Neural Networks

Introduction

The codes are simple re-implement version for paper: Interleaved Group Convolutions for Deep Neural Networks. (IGCV V1)

Zhang T, Qi G J, Xiao B, et al. Interleaved Group Convolutions for Deep Neural Networks[J]. 2017. arXiv:1707.02725

Some details are different from the description in origin paper.

Structure

We present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary.

Our motivation comes from the four branch presentation of regular convolution illustrated in the following picture.

Requirements

• jupyter notebook
• Python3
• PyTorch 0.3

Results

We just test IGCV_L24M2 in two datasets: Cifar10 and Tiny ImageNet

Cifar-10

Models	train(Top-1)	validation(Top-1)	L	M	D
IGCV_L24M2	99.4	91.8	24	2	20

Tiny ImageNet

Models	train(Top-1)	validation(Top-1)	L	M	D
IGCV_L24M2	71.2	58.1	24	2	20