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https://github.com/ShichenLiu/CondenseNet

CondenseNet: Light weighted CNN for mobile devices
https://github.com/ShichenLiu/CondenseNet

deep-learning mobile-device pytorch

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CondenseNet: Light weighted CNN for mobile devices

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README 

        
# CondenseNets



This repository contains the code (in PyTorch) for "[CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://arxiv.org/abs/1711.09224)" paper by [Gao Huang](http://www.cs.cornell.edu/~gaohuang/)\*, [Shichen Liu](https://shichenliu.github.io)\*, [Laurens van der Maaten](https://lvdmaaten.github.io) and [Kilian Weinberger](https://www.cs.cornell.edu/%7Ekilian/) (* Authors contributed equally).



### Citation



If you find our project useful in your research, please consider citing:



```

@inproceedings{huang2018condensenet,

  title={Condensenet: An efficient densenet using learned group convolutions},

  author={Huang, Gao and Liu, Shichen and Van der Maaten, Laurens and Weinberger, Kilian Q},

  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},

  pages={2752--2761},

  year={2018}

}

```



## Contents



1. [Introduction](#introduction)

2. [Usage](#usage)

3. [Results](#results)

4. [Discussions](#discussions)

5. [Contacts](#contacts)



## Introduction



CondenseNet is a novel, computationally efficient convolutional network architecture. It combines dense connectivity between layers with a mechanism to remove unused connections. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard grouped convolutions —- allowing for efficient computation in practice. Our experiments demonstrate that CondenseNets are much more efficient than other compact convolutional networks such as MobileNets and ShuffleNets.






Figure 1: Learned Group Convolution with G=C=3.






Figure 2: CondenseNets with Fully Dense Connectivity and Increasing Growth Rate.



## Usage



### Dependencies



- [Python3](https://www.python.org/downloads/)

- [PyTorch(1.1.0)](http://pytorch.org)

- [ImageNet](https://www.image-net.org/challenges/LSVRC/2012/)



### Train

As an example, use the following command to train a CondenseNet on ImageNet



```

python main.py --model condensenet -b 256 -j 20 /PATH/TO/IMAGENET \

--stages 4-6-8-10-8 --growth 8-16-32-64-128 --gpu 0,1,2,3,4,5,6,7 --resume

```



As another example, use the following command to train a CondenseNet on CIFAR-10



```

python main.py --model condensenet -b 64 -j 12 cifar10 \

--stages 14-14-14 --growth 8-16-32 --gpu 0 --resume

```



### Evaluation

We take the ImageNet model trained above as an example.



To evaluate the trained model, use `evaluate` to evaluate from the default checkpoint directory:



```

python main.py --model condensenet -b 64 -j 20 /PATH/TO/IMAGENET \

--stages 4-6-8-10-8 --growth 8-16-32-64-128 --gpu 0 --resume \

--evaluate

```



or use `evaluate-from` to evaluate from an arbitrary path:



```

python main.py --model condensenet -b 64 -j 20 /PATH/TO/IMAGENET \

--stages 4-6-8-10-8 --growth 8-16-32-64-128 --gpu 0 --resume \

--evaluate-from /PATH/TO/BEST/MODEL

```



Note that these models are still the large models. To convert the model to group-convolution version as described in the paper, use the `convert-from` function:



```

python main.py --model condensenet -b 64 -j 20 /PATH/TO/IMAGENET \

--stages 4-6-8-10-8 --growth 8-16-32-64-128 --gpu 0 --resume \

--convert-from /PATH/TO/BEST/MODEL

```



Finally, to directly load from a converted model (that is, a CondenseNet), use a **converted model file** in combination with the `evaluate-from` option:



```

python main.py --model condensenet_converted -b 64 -j 20 /PATH/TO/IMAGENET \

--stages 4-6-8-10-8 --growth 8-16-32-64-128 --gpu 0 --resume \

--evaluate-from /PATH/TO/CONVERTED/MODEL

```



### Other Options

We also include DenseNet implementation in this repository.  

For more examples of usage, please refer to [script.sh](script.sh)  

For detailed options, please `python main.py --help`



## Results



### Results on ImageNet



| Model | FLOPs | Params | Top-1 Err. | Top-5 Err. | Pytorch Model |

|---|---|---|---|---|---|

| CondenseNet-74 (C=G=4) | 529M | 4.8M | 26.2 | 8.3 | [Download (18.69M)](https://www.dropbox.com/s/sj26rm4so3uhdmg/converted_condensenet_4.pth.tar?dl=0) |

| CondenseNet-74 (C=G=8) | 274M | 2.9M | 29.0 | 10.0 | [Download (11.68M)](https://www.dropbox.com/s/aj1xpd6zcnclous/converted_condensenet_8.pth.tar?dl=0) |



### Results on CIFAR



| Model | FLOPs | Params | CIFAR-10 | CIFAR-100 |

|---|---|---|---|---|

| CondenseNet-50 | 28.6M | 0.22M | 6.22 | - |

| CondenseNet-74 | 51.9M | 0.41M | 5.28 | - |

| CondenseNet-86 | 65.8M | 0.52M | 5.06 | 23.64 |

| CondenseNet-98 | 81.3M | 0.65M | 4.83 | - |

| CondenseNet-110 | 98.2M | 0.79M | 4.63 | - |

| CondenseNet-122 | 116.7M | 0.95M | 4.48 | - |

| CondenseNet-182* | 513M | 4.2M | 3.76 | 18.47 |



(* trained 600 epochs)



### Inference time on ARM platform



| Model | FLOPs | Top-1 | Time(s) |

|---|---|---|---|

| VGG-16 | 15,300M | 28.5 | 354 |

| ResNet-18 | 1,818M | 30.2 | 8.14 |

| 1.0 MobileNet-224 | 569M | 29.4 | 1.96 |

| CondenseNet-74 (C=G=4) | 529M | 26.2 | 1.89 |

| CondenseNet-74 (C=G=8) | 274M | 29.0 | 0.99 |



## Contact

[email protected]  

[email protected]



We are working on the implementation on other frameworks.  

Any discussions or concerns are welcomed!