IGCV3:Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks.

The codes are based on https://github.com/liangfu/mxnet-mobilenet-v2.

Prior Works

Interleaved Group Convolutions (IGCV1)

Interleaved Group Convolutions use a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary.

Illustrating the interleaved group convolution, with L = 2 primary partitions and M = 3 secondary partitions. The convolution for each primary partition in primary group convolution is spatial. The convolution for each secondary partition in secondary group convolution is point-wise (1 × 1).

You can find its code here!

Interleaved Structured Sparse Convolution (IGCV2)

IGCV2 extends IGCV1 by decomposing the convolution matrix in to more structured sparse matrices, which uses a depth-wise convoultion (3 × 3) to replace the primary group convoution in IGC and uses a series of point-wise group convolutions (1 × 1).

Interleaved Low-Rank Group Convolutions (IGCV3)

We proposes Interleaved Low-Rank Group Convolutions, named IGCV3, extend IGCV2 by using low-rank group convolutions to replace group convoutions in IGCV2. It consists of a channel-wise spatial convolution, a low-rank group convolution with groups that reduces the width and a low-rank group convolution with groups which expands the widths back.

Illustrating the interleaved branches in IGCV3 block. The first group convolution is a group 1 × 1 convolution with =2 groups. The second is a channel-wise spatial convolution. The third is a group 1 × 1 convolution with =2 groups.

Results

CIFAR Experiments

We compare our IGCV3 network with other Mobile Networks on CIFAR datasets which illustrated our model' advantages on small dataset.

Comparison with Other Mobile Networks

Classification accuracy comparisons of MobileNetV2 and IGCV3 on CIFAR datasets. "Network s×" means reducing the number of parameter in "Network 1.0×" by s times.

Comparison with IGCV2

ImageNet Experiments

Comparison with MobileNetV2 on ImageNet.

Before Retrain

After Retrain

IGCV3 pretrained model is released in models folder.

Requirements

• Install MXNet

How to Train

Current code supports training IGCV3s on ImageNet. All the networks are contained in the symbol folder.

For example, running the following command can train the IGCV3 network on ImageNet.

python train_imagenet.py --network=IGCV3 --multiplier=1.0 --gpus=0,1,2,3,4,5,6,7 --batch-size=96 --data-dir=<dataset location>

multiplier is means how many times wider than the original IGCV3 network whose width is the same as MobileNet-V2.

Citation

Please cite our papers in your publications if it helps your research:

@article{WangWZZ16,
  author    = {Jingdong Wang and
               Zhen Wei and
               Ting Zhang and
               Wenjun Zeng},
  title     = {Deeply-Fused Nets},
  journal   = {CoRR},
  volume    = {abs/1605.07716},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.07716}
}

@article{ZhaoWLTZ16,
  author    = {Liming Zhao and
               Jingdong Wang and
               Xi Li and
               Zhuowen Tu and
               Wenjun Zeng},
  title     = {On the Connection of Deep Fusion to Ensembling},
  journal   = {CoRR},
  volume    = {abs/1611.07718},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.07718}
}

@article{DBLP:journals/corr/ZhangQ0W17,
  author    = {Ting Zhang and
               Guo{-}Jun Qi and
               Bin Xiao and
               Jingdong Wang},
  title     = {Interleaved Group Convolutions for Deep Neural Networks},
  journal   = {ICCV},
  volume    = {abs/1707.02725},
  year      = {2017},
  url       = {http://arxiv.org/abs/1707.02725}
}

@article{DBLP:journals/corr/abs-1804-06202,
  author    = {Guotian Xie and
               Jingdong Wang and
               Ting Zhang and
               Jianhuang Lai and
               Richang Hong and
               Guo{-}Jun Qi},
  title     = {{IGCV2:} Interleaved Structured Sparse Convolutional Neural Networks},
  journal   = {CVPR},
  volume    = {abs/1804.06202},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.06202},
  archivePrefix = {arXiv},
  eprint    = {1804.06202},
  timestamp = {Wed, 02 May 2018 15:55:01 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1804-06202},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}