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https://github.com/homles11/IGCV3

Code and Pretrained model for IGCV3
https://github.com/homles11/IGCV3

igcv3 image-classification

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Code and Pretrained model for IGCV3

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README 

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

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

>  IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. 

arXiv preprint [arXIV:1806.00178](https://arxiv.org/pdf/1806.00178.pdf) (2017)



## Prior Works



### Interleaved Group Convolutions ([IGCV1](https://arxiv.org/pdf/1707.02725.pdf))

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.



![IGC](figures/igc_ori.png)

>  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](https://github.com/hellozting/InterleavedGroupConvolutions)!



### Interleaved Structured Sparse Convolution ([IGCV2](https://arxiv.org/pdf/1804.06202.pdf))

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.



![IGCV3](figures/super_branch_2.PNG)

>  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.

 

 #Params (M) CIFAR-10  CIFAR100   

 MobileNetV2（our impl.）   2.394.56 77.09

 IGCV3-D 0.5×   1.294.73 77.29

 IGCV3-D 0.7×  1.794.92 77.83

 IGCV3-D 1.0×  2.494.96 77.95



#### Comparison with IGCV2

 

 #Params (M) CIFAR-10  CIFAR100   

 IGCV2  2.494.76 77.45

 IGCV3-D  2.494.96 77.95



### ImageNet Experiments

Comparison with MobileNetV2 on ImageNet.

#### Before Retrain

 

 #Params (M) Top-1 Top-5  

 MobileNetV2  3.470.0 89.0

 IGCV3-D  3.570.6 89.7



#### After Retrain

 

 #Params (M) Top-1 Top-5  

  MobileNetV2  3.471.4 90.1

 IGCV3-D   3.5 72.2 90.5



**IGCV3 pretrained model is released in `models` folder.**

## Requirements

- Install [MXNet](https://mxnet.incubator.apache.org/install/index.html)



## 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.



```shell

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

```

`multiplier` is means how many times wider than the original IGCV3 network whose width is the same as [MobileNet-V2](https://arxiv.org/pdf/1801.04381).



## 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}

}

```

```

@article{KeSun18,

  author    = {Ke Sun and

               Mingjie Li and

               Dong Liu and

               Jingdong Wang},

  title     = {IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks},

  journal   = {CoRR},

  volume    = {abs/1806.00178},

  year      = {2018},

  url       = {http://arxiv.org/abs/1806.00178}

}

```